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Showing papers on "Linear discriminant analysis published in 2016"


Journal ArticleDOI
TL;DR: This work proposes a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms.
Abstract: In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple linear transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.

610 citations


Journal ArticleDOI
TL;DR: Interestingly, using only one predictor (temperature) the LDA model was able to estimate the occupancy with accuracies of 85% and 83% in the two testing sets.

455 citations


Journal ArticleDOI
TL;DR: In this paper, the authors compare the reliability of three classes of dissimilarity measures: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance, using simulations and four real functional magnetic resonance imaging (fMRI) datasets.

416 citations


Journal ArticleDOI
TL;DR: Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment, but it has been observed that the SVM model has the best performance in comparison to other landslide models.
Abstract: Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naive Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUCź=ź0.910-0.950). However, it has been observed that the SVM model (AUCź=ź0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUCź=ź0.922), the FLDA model (AUCź=ź0.921), the BN model (AUCź=ź0.915), and the NB model (AUCź=ź0.910), respectively. Machine learning methods namely SVM, LR, FLDA, BN, and NB have been evaluated and compared for landslide susceptibility assessment.Results indicate that all these five models can be applied efficiently for landslide assessment and prediction.Analysis of comparative results reaffirmed that the SVM model is one of the best methods.

363 citations


Journal ArticleDOI
TL;DR: A data set of 246 rockburst events was examined for rockburst classification using supervised learning (SL) methods and 11 algorithms from 10 categories of SL algorithms were evaluated for their ability to learn rockburst.
Abstract: Rockburst prediction is of crucial importance to the design and construction of many underground projects. Insufficient knowledge, lack of characterizing information, and noisy data restrain rock mechanics engineers from achieving optimal prediction results. In this paper, a data set of 246 rockburst events was examined for rockburst classification using supervised learning (SL) methods. The data set was analyzed with 8 potentially relevant indicators. Eleven algorithms from 10 categories of SL algorithms were evaluated for their ability to learn rockburst, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLSDA), naive Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron neural network (MLPNN), classification tree (CT), support vector machine (SVM), random forest (RF), and gradient-boosting machine (GBM). The data set was randomly split into two parts: training (70%) and test (30%). A 10-fold cross-validation (...

279 citations


Journal ArticleDOI
TL;DR: Experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed ensemble method can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting.
Abstract: Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).

272 citations


Journal ArticleDOI
TL;DR: The theory of robust regression (RR) is developed and an effective convex approach that uses recent advances on rank minimization is presented that applies to a variety of problems in computer vision including robust linear discriminant analysis, regression with missing data, and multi-label classification.
Abstract: Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( ${\mathbf X}$ ) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that samples are directly projected onto a subspace and hence fail to account for outliers common in realistic training sets due to occlusion, specular reflections or noise. It is important to notice that existing discriminative approaches assume the input variables ${\mathbf X}$ to be noise free. Thus, discriminative methods experience significant performance degradation when gross outliers are present. Despite its obvious importance, the problem of robust discriminative learning has been relatively unexplored in computer vision. This paper develops the theory of robust regression (RR) and presents an effective convex approach that uses recent advances on rank minimization. The framework applies to a variety of problems in computer vision including robust linear discriminant analysis, regression with missing data, and multi-label classification. Several synthetic and real examples with applications to head pose estimation from images, image and video classification and facial attribute classification with missing data are used to illustrate the benefits of RR.

268 citations


Journal ArticleDOI
12 Sep 2016
TL;DR: The aim of this paper is to collect in one place the basic background needed to understand the discriminant analysis (DA) classifier to make the reader of all levels be able to get a better understanding of the DA and to know how to apply this classifier in different applications.
Abstract: The aim of this paper is to collect in one place the basic background needed to understand the discriminant analysis (DA) classifier to make the reader of all levels be able to get a better understanding of the DA and to know how to apply this classifier in different applications. This paper starts with basic mathematical definitions of the DA steps with visual explanations of these steps. Moreover, in a step-by-step approach, a number of numerical examples were illustrated to show how to calculate the discriminant functions and decision boundaries when the covariance matrices of all classes were common or not. The singularity problem of DA was explained and some of the state-of-the-art solutions to this problem were highlighted with numerical illustrations. An experiment is conducted to compare between the linear and quadratic classifiers and to show how to solve the singularity problem when high-dimensional datasets are used.

205 citations


Journal ArticleDOI
TL;DR: A novel compound rank-k projection (CRP) algorithm for bilinear analysis that deals with matrices directly without transforming them into vectors, and it preserves the correlations within the matrix and decreases the computation complexity.
Abstract: In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing 2-D discriminant analysis algorithms employ a single projection model to exploit the discriminant information for projection, making the model less flexible. In this paper, we propose a novel compound rank- $k$ projection (CRP) algorithm for bilinear analysis. The CRP deals with matrices directly without transforming them into vectors, and it, therefore, preserves the correlations within the matrix and decreases the computation complexity. Different from the existing 2-D discriminant analysis algorithms, objective function values of CRP increase monotonically. In addition, the CRP utilizes multiple rank- $k$ projection models to enable a larger search space in which the optimal solution can be found. In this way, the discriminant ability is enhanced. We have tested our approach on five data sets, including UUIm, CVL, Pointing’04, USPS, and Coil20. Experimental results show that the performance of our proposed CRP performs better than other algorithms in terms of classification accuracy.

170 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors combined two different methodological approaches to discriminative feature selection in a unified framework, namely, linear discriminant analysis and locality preserving projection, to select class-discriminative and noise-resistant features.
Abstract: The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.

166 citations


Journal ArticleDOI
TL;DR: It is explained that discriminant methods do a poor authentication job because of their inability of proper classification of new samples, which do not belong to any of the predefined classes.
Abstract: Authentication is the process of determining whether an object is, in fact, what it is declared to be. In practice, this problem is often solved by using discriminant methods. In this paper, we explain that such techniques do a poor authentication job. The main drawback of discriminant methods is their inability of proper classification of new samples, which do not belong to any of the predefined classes. Our considerations are illustrated by a real-world example and a comparison of the results provided by the following two methods: Partial Least Squares- Discriminant Analysis, PLS-DA, and Data Driven Soft Independent Modeling of Class Analogy, DD-SIMCA.

Journal ArticleDOI
15 Dec 2016-PLOS ONE
TL;DR: This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease by investigating pattern recognition algorithms based on image-processing technology and building semi-supervised models for disease recognition.
Abstract: Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Journal ArticleDOI
01 Jul 2016
TL;DR: The present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor that decomposes the single-Channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm.
Abstract: An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

Proceedings ArticleDOI
27 Jun 2016
TL;DR: A novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM) is proposed, which embeds the synchronous learning of multiple discriminative subspaces into multi- view K- means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspacing simultaneously.
Abstract: In real world applications, more and more data, for example, image/video data, are high dimensional and repre-sented by multiple views which describe different perspectives of the data. Efficiently clustering such data is a challenge. To address this problem, this paper proposes a novel multi-view clustering method called Discriminatively Embedded K-Means (DEKM), which embeds the synchronous learning of multiple discriminative subspaces into multi-view K-Means clustering to construct a unified framework, and adaptively control the intercoordinations between these subspaces simultaneously. In this framework, we firstly design a weighted multi-view Linear Discriminant Analysis (LDA), and then develop an unsupervised optimization scheme to alternatively learn the common clustering indicator, multiple discriminative subspaces and weights for heterogeneous features with convergence. Comprehensive evaluations on three benchmark datasets and comparisons with several state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.

Journal ArticleDOI
TL;DR: In this article, a two-stage data-driven FDD strategy is proposed to detect and diagnose chiller faults in order to save energy and improve the performance of building automation systems, which formulates the chiller detection and diagnosis task as a multi-class classification problem.

Journal ArticleDOI
TL;DR: In this paper, the authors used the K-nearest neighbors method to identify 5 different gear crack levels under different motor speeds and loads, and showed that the developed method has the highest prediction accuracies among these statistical models, including linear discriminant analysis, quadratic discriminant analyses, classification and regression tree and naive Bayes classifier.

Journal ArticleDOI
TL;DR: ‘compliant’ and ‘rigorous’ approaches are critically compared on real case studies, by applying two novel modelling techniques: partial least squares density modelling (PLS-DM) and data driven soft independent modelling of class analogy (DD-SIMCA).

Journal ArticleDOI
Jianbo Yu1, Xiaolei Lu1
TL;DR: In this article, a manifold learning-based wafer map defect detection and recognition system is proposed to discover intrinsic manifold information that provides the discriminant characteristics of the defect patterns, and an unsupervised version of JLNDA is further developed to provide a monitoring chart for defect detection of wafer maps.
Abstract: In semiconductor manufacturing processes, defect detection and recognition in wafer maps have received increasing attention from semiconductor industry. The various defect patterns in wafer maps provide crucial information for assisting engineers in recognizing the root causes of the fabrication problems and solving them eventually. This paper develops a manifold learning-based wafer map defect detection and recognition system. In this system, a joint local and nonlocal linear discriminant analysis (JLNDA) is proposed to discover intrinsic manifold information that provides the discriminant characteristics of the defect patterns. An unsupervised version of JLNDA (called local and nonlocal preserving projection) is further developed to provide a monitoring chart for defect detection of wafer maps. A JLNDA-based Fisher discriminant is further put forward for online defect recognition without the modeling procedure of recognizers. Comparisons with other regular methods, principal component analysis, local preserving projection, linear discriminant analysis (LDA) and local LDA, illustrate the superiority of JLNDA in wafer map defect recognition. The effectiveness of the proposed system has been verified by experimental results from a real-world data set of wafer maps (WM-811K).

Journal ArticleDOI
01 Jan 2016
TL;DR: An attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step, and an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA) is proposed.
Abstract: A novel adaptive cuckoo search algorithm, without using a Levy step, is proposed.The new algorithm improves the objective function values with a faster rate.Our evolutionary face recognition algorithm provides improved recognition rate.Optimal dimension reduction is achieved using PCA+IDA algorithm.ACS-IDA algorithm search optimal eigenvectors to improve accuracy. This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA+IDA and ACS-IDA. Interestingly, PCA+IDA offers us a perturbation free algorithm for dimension reduction while ACS+IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases-YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.

Journal ArticleDOI
TL;DR: A new supervised learning method called multiview ULDA (MULDA) is proposed, which combines the theory of ULDA with CCA and generalizes these methods to the nonlinear case by kernel-based learning techniques.
Abstract: Multiview learning is more robust than single-view learning in many real applications. Canonical correlation analysis (CCA) is a popular technique to utilize information stemming from multiple feature sets. However, it does not exploit label information effectively. Later multiview linear discriminant analysis (MLDA) was proposed through combining CCA and linear discriminant analysis (LDA). Due to the successful application of uncorrelated LDA (ULDA), which seeks optimal discriminant features with minimum redundancy, we propose a new supervised learning method called multiview ULDA (MULDA) in this paper. This method combines the theory of ULDA with CCA. Then we adapt discriminant CCA (DCCA) instead of the CCA in MLDA and MULDA, and discuss about the effect of this modification. Furthermore, we generalize these methods to the nonlinear case by kernel-based learning techniques. The new method is called kernel multiview uncorrelated discriminant analysis (KMUDA). Then we modify kernel multiview discriminant analysis and KMUDA by replacing Kernel CCA with Kernel DCCA. Our methods are tested on different real datasets and compared with other state-of-the-art methods. Experimental results validate the effectiveness of our methods.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum, which is evaluated by means of linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier.
Abstract: Bearing damage is the most common failure in electrical machines. It can be detected by vibration analysis. However, this diagnosis method is costly or not always accessible due to the location of the equipment and the choice of the implemented sensors. An alternative method is provided with the electrical monitoring using the stator current of the electrical machine. This study aims at developing a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum. The features are evaluated by means of the linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier. The proposed method is evaluated by two types of damages at different load cases. The results show that the damaged bearings can be distinguished from the healthy bearing depending on the considered load cases.

Journal ArticleDOI
TL;DR: A novel method is proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy and it is shown that the proposed method is well suited and effective for bearing defect classification.
Abstract: In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.

Journal ArticleDOI
TL;DR: Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA.
Abstract: Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by $\ell_{1}$ -norm optimization based on available labeled samples. Different from traditional methods (e.g., $k$ -nearest neighbor with Euclidean distance), weights in an $\ell_{1}$ -graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA.

Journal ArticleDOI
TL;DR: This paper proposes a novel method named semi-supervised linear discriminant analysis (SLDA), which can use limited number of labeled data and a quantity of the unlabeled ones for training so that LDA can accommodate to the situation of a few labeled data available.

Posted Content
TL;DR: Correlation alignment (CORAL) as mentioned in this paper aligns the second-order statistics of source and target distributions without requiring any target labels, which is a simple yet effective method for unsupervised domain adaptation.
Abstract: In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is also much simpler than other distribution matching methods. CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. We first describe a solution that applies a linear transformation to source features to align them with target features before classifier training. For linear classifiers, we propose to equivalently apply CORAL to the classifier weights, leading to added efficiency when the number of classifiers is small but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA) outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (DNNs). The resulting Deep CORAL approach works seamlessly with DNNs and achieves state-of-the-art performance on standard benchmark datasets. Our code is available at:~\url{this https URL}

Journal ArticleDOI
01 Mar 2016
TL;DR: A novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data.
Abstract: In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincare section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.

Journal ArticleDOI
TL;DR: This work analyzes and compares the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals using HbO signals to validate the results.
Abstract: We analyse and compare the classification accuracies of six different classifiers for a two-class mental task mental arithmetic and rest using functional near-infrared spectroscopy fNIRS signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin HbO signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis LDA, quadratic discriminant analysis QDA, k-nearest neighbour kNN, the Naive Bayes approach, support vector machine SVM, and artificial neural networks ANN, were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers p < 0.005 using HbO signals.

Journal ArticleDOI
TL;DR: This paper proposes a new HS image classification method that uses matrix-based spatial-spectral feature representation for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-Spectral correlation.
Abstract: Spatial–spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial–spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial–spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial–spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method.

Journal ArticleDOI
TL;DR: This work addresses three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA), and argues that RFs should be taken into consideration for future BCIs.
Abstract: There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

Journal ArticleDOI
TL;DR: A novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm to improve its convergence behavior in both sides, accelerate the convergence process for accessing the optimum steady-state and minimizing the error misadjustment to improve the recognized patterns superiorly.
Abstract: Back-propagation neural network (BPNN).Variable Adaptive Momentum.Supervised Learning Algorithms.Deep Learning. In this paper, we propose a novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm. The proposed algorithm is a modified version of the BP algorithm to improve its convergence behavior in both sides, accelerate the convergence process for accessing the optimum steady-state and minimizing the error misadjustment to improve the recognized patterns superiorly. This algorithm is controlled by the learning rate parameter which is dependent on the eigenvalues of the autocorrelation matrix of the input. It provides low error performance for the weights update. To discuss the performance measures of this proposed algorithm and the other supervised learning algorithms such as k-nearest neighbours (k-NN), Naive Bayes (NB), linear discriminant analysis (LDA), support vector machines (SVM), BP, and BP with adaptive momentum (PBPAM) have been compared in term of speed of convergence, Sum of Squared Error (SSE), and accuracy by implementing benchmark problem - XOR and seven datasets from UCI repository.