Showing papers on "Sparse approximation published in 2019"
TL;DR: This paper proposes a new neural network for anomaly detection by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM).
Abstract: Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the advantages of eliminating noisy background, capturing motion, and alleviating data deficiency. Furthermore, to address some disadvantages (e.g., nonadaptive updating) of the existing sparse coding optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel recurrent neural network to learn sparse representation and dictionary by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM). To the best of our knowledge, this could be one of the first works to bridge the $\ell _{1}$ - solver and LSTM and may provide novel insight into understanding LSTM and model-based optimization (or named differentiable programming), as well as sparse coding-based anomaly detection. Extensive experiments show the state-of-the-art performance of our method in the abnormal events detection task.
218 citations
TL;DR: Experimental results demonstrate that the proposed CS-MCA model can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
Abstract: In this letter, a sparse representation (SR) model named convolutional sparsity based morphological component analysis (CS-MCA) is introduced for pixel-level medical image fusion. Unlike the standard SR model, which is based on single image component and overlapping patches, the CS-MCA model can simultaneously achieve multi-component and global SRs of source images, by integrating MCA and convolutional sparse representation (CSR) into a unified optimization framework. For each source image, in the proposed fusion method, the CSRs of its cartoon and texture components are first obtained by the CS-MCA model using pre-learned dictionaries. Then, for each image component, the sparse coefficients of all the source images are merged and the fused component is accordingly reconstructed using the corresponding dictionary. Finally, the fused image is calculated as the superposition of the fused cartoon and texture components. Experimental results demonstrate that the proposed method can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
190 citations
TL;DR: This paper addresses the more challenging case of not only using a single camera but also not leveraging markers: going directly from 2D appearance to 3D geometry, using a novel approach that treats 2D joint locations as latent variables whose uncertainty distributions are given by a deep fully convolutional neural network.
Abstract: Recovering 3D full-body human pose is a challenging problem with many applications. It has been successfully addressed by motion capture systems with body worn markers and multiple cameras. In this paper, we address the more challenging case of not only using a single camera but also not leveraging markers: going directly from 2D appearance to 3D geometry. Deep learning approaches have shown remarkable abilities to discriminatively learn 2D appearance features. The missing piece is how to integrate 2D, 3D, and temporal information to recover 3D geometry and account for the uncertainties arising from the discriminative model. We introduce a novel approach that treats 2D joint locations as latent variables whose uncertainty distributions are given by a deep fully convolutional neural network. The unknown 3D poses are modeled by a sparse representation and the 3D parameter estimates are realized via an Expectation-Maximization algorithm, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Extensive evaluation on benchmark datasets shows that the proposed approach achieves greater accuracy over state-of-the-art baselines. Notably, the proposed approach does not require synchronized 2D-3D data for training and is applicable to “in-the-wild” images, which is demonstrated with the MPII dataset.
175 citations
TL;DR: Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
Abstract: This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor–tensor product ( ${t-product}$ )-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using ${t-product}$ , which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
155 citations
TL;DR: The proposed iterative channel estimation algorithm based on the least square estimation (LSE) and sparse message passing (SMP) algorithm for the millimeter wave (mmWave) MIMO systems has much better performance than the existing sparse estimators, especially when the channel is sparse.
Abstract: We propose an iterative channel estimation algorithm based on the least square estimation (LSE) and sparse message passing (SMP) algorithm for the millimeter wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli–Gaussian distribution and the channel matrix is sparse with only a few nonzero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of nonzero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cramer–Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations.
140 citations
TL;DR: A novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed, based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults.
Abstract: Bearing faults are one of the most common inducements for machine failures. Therefore, it is very important to perform bearing fault diagnosis reliably and rapidly. However, it is fundamental but difficult to extract impulses buried in heavy background noise for bearing fault diagnosis. In this paper, a novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed. The algorithm is based on the proposed enhanced sparse group lasso penalty, which promotes the sparsity within and across groups of the impulsive feature of bearing faults. Moreover, a periodic prior is embedded and updated dynamically through each iteration of the optimization procedure. Additionally, we formed a deterministic rule about how to set the parameters adaptively. The main advantage over conventional sparse representation methods is that AdaESPGL is parameter free (forming a deterministic rule) and rapid (extracting the impulsive information directly from the time domain). Finally, the performance of AdaESPGL is verified through a series of numerical simulations and the diagnosis of a motor bearing. Results demonstrate its superiority in extracting periodic impulses in comparison to other state-of-the-art methods.
139 citations
01 Oct 2019
TL;DR: A deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds with comparable visual results with the state-of-the-art methods but in much shorter running time is proposed.
Abstract: Snapshot compressive imaging (SCI) systems have been developed to capture high-dimensional ($\ge$ 3) signals using low-dimensional off-the-shelf sensors, \ie, mapping multiple video frames into a single measurement frame. One key module of a SCI system is an accurate decoder that recovers the original video frames. However, existing model-based decoding algorithms require exhaustive parameter tuning with prior knowledge and cannot support practical applications due to the extremely long running time. In this paper, we propose a deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds. Firstly, we start with a standard tensor ADMM algorithm, unfold its inference iterations into a layer-wise structure, and design a deep neural network based on tensor operations. Secondly, instead of relying on a pre-specified sparse representation domain, the network learns the domain of low-rank tensor through stochastic gradient descent. It is worth noting that the proposed deep tensor ADMM-Net has potentially mathematical interpretations. On public video data, the simulation results show the proposed {method} achieves average $0.8 \sim 2.5$ dB improvement in PSNR and $0.07 \sim 0.1$ in SSIM, and $1500\times \sim 3600 \times$ speedups over the state-of-the-art methods. On real data captured by SCI cameras, the experimental results show comparable visual results with the state-of-the-art methods but in much shorter running time.
116 citations
TL;DR: A maximum likelihood estimation (MLE-based JSR) model, which replaces the traditional quadratic loss function with an MLE-like estimator for measuring the joint approximation error and demonstrates the effectiveness of the proposed MLEJSR method, especially in the case of large noise.
Abstract: A joint sparse representation (JSR) method has shown superior performance for the classification of hyperspectral images (HSIs). However, it is prone to be affected by outliers in the HSI spatial neighborhood. In order to improve the robustness of JSR, we propose a maximum likelihood estimation (MLE)-based JSR (MLEJSR) model, which replaces the traditional quadratic loss function with an MLE-like estimator for measuring the joint approximation error. The MLE-like estimator is actually a function of coding residuals. Given some priors on the coding residuals, the MLEJSR model can be easily converted to an iteratively reweighted JSR problem. Choosing a reasonable weight function, the effect of inhomogeneous neighboring pixels or outliers can be dramatically reduced. We provide a theoretical analysis of MLEJSR from the viewpoint of recovery error and evaluate its empirical performance on three public hyperspectral data sets. Both the theoretical and experimental results demonstrate the effectiveness of our proposed MLEJSR method, especially in the case of large noise.
115 citations
TL;DR: In this paper, the authors investigated the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work, and showed that NR can boost the representation power of homogeneous samples while limiting the represent power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR.
105 citations
TL;DR: A self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs) and is more accurate and robust than existing JSR methods, especially in the case of heavy noise.
Abstract: In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs) It replaces the least-squares (LS) loss in the standard joint sparse representation (JSR) model with a weighted LS loss and adopts a self-paced learning (SPL) strategy to learn the weights for neighboring pixels Rather than predefining a weight vector in the existing weighted JSR methods, both the weight and sparse representation (SR) coefficient associated with neighboring pixels are optimized by an alternating iterative strategy According to the nature of SPL, in each iteration, neighboring pixels with nonzero weights (ie, easy pixels) are included for the joint SR of a testing pixel With the increase of iterations, the model size (ie, the number of selected neighboring pixels) is enlarged and more neighboring pixels from easy to complex are gradually added into the JSR learning process After several iterations, the algorithm can be terminated to produce a desirable model that includes easy homogeneous pixels and excludes complex inhomogeneous pixels Experimental results on two benchmark hyperspectral data sets demonstrate that our proposed SPJSR is more accurate and robust than existing JSR methods, especially in the case of heavy noise
103 citations
TL;DR: Experiments show that the proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.
Abstract: In this paper, we propose a new anomaly detection method for hyperspectral images based on two well-designed dictionaries: background dictionary and potential anomaly dictionary. In order to effectively detect an anomaly and eliminate the influence of noise, the original image is decomposed into three components: background, anomalies, and noise. In this way, the anomaly detection task is regarded as a problem of matrix decomposition. Considering the homogeneity of background and the sparsity of anomalies, the low-rank and sparse constraints are imposed in our model. Then, the background and potential anomaly dictionaries are constructed using the background and anomaly priors. For the background dictionary, a joint sparse representation (JSR)-based dictionary selection strategy is proposed, assuming that the frequently used atoms in the overcomplete dictionary tend to be the background. In order to make full use of the prior information of anomalies hidden in the scene, the potential anomaly dictionary is constructed. We define a criterion, i.e., the anomalous level of a pixel, by using the residual calculated in the JSR model within its local region. Then, it is combined with a weighted term to alleviate the influence of noise and background. Experiments show that our proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.
TL;DR: This paper proposes a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region.
Abstract: Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
TL;DR: The novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection and outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.
Abstract: Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.
TL;DR: A framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively is proposed and shown to outperforms the conventional and typical ship tracking methods.
Abstract: Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively Finally, our framework is evaluated in four typical maritime surveillance scenarios The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods
TL;DR: The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.
Abstract: The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized $$l_{1}$$ norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the $$l_{1}$$ norm based solving algorithm, $$l_{2}$$ norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum $$l_{2}$$ norm method to select the local dictionary. Then the minimum $$l_{1}$$ norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNN-SRC algorithm.
TL;DR: A parametric impulsive dictionary designed for bearing fault feature extraction is designed and the parameters of the Laplace wavelets, which are highly matched with the local bearing fault features, are discretized by the modified alternating projection method.
TL;DR: Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited, and 21 well-known methods are adopted to be compared.
Abstract: In this article, a novel hyperspectral image (HSI) classification method based on fusing multiple edge-preserving operations (EPOs) is proposed, which consists of the following steps. First, the edge-preserving features are obtained by performing different types of EPOs, i.e., local edge-preserving filtering and global edge-preserving smoothing on the dimension-reduced HSI. Then, with the assistance of a superpixel segmentation method, the edge-preserving features are further improved by considering the inter and intra spectral properties of superpixels. Finally, the spectral and edge-preserving features are fused to form one composite kernel, which is fed into the support vector machine (SVM) followed by a majority voting fusion scheme. Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited. Furthermore, 21 well-known methods, including mathematical morphology-based approaches, sparse representation models, and deep learning-based classifiers, are adopted to be compared with the proposed method on Houston data set with standard sets of training and test samples released during 2013 Data Fusion Contest, which also shows the effectiveness of the proposed method.
TL;DR: This paper proposes a novel spatial-spectral sparse representation (SSSR) based approach for the fusion of an HR-MSI and an LR-HSI of the same scenario, and designs the alternative optimization algorithm for the estimation of spectral basis and coefficients, which can achieve the accurate reconstruction.
TL;DR: A novel coupled sparse autoencoder (CSAE) is proposed in this paper to effectively learn the mapping relation between the LR and HR images and has gained solid improvements in terms of average peak signal-to-noise ratio and structural similarity measurement.
Abstract: Remote sensing image super-resolution (SR) refers to a technique improving the spatial resolution, which in turn benefits to the subsequent image interpretation, e.g., target recognition, classification, and change detection. In popular sparse representation-based methods, due to the complex imaging conditions and unknown degradation process, the sparse coefficients of low-resolution (LR) observed images are hardly consistent with the real high-resolution (HR) counterparts, which leads to unsatisfactory SR results. To address this problem, a novel coupled sparse autoencoder (CSAE) is proposed in this paper to effectively learn the mapping relation between the LR and HR images. Specifically, the LR and HR images are first represented by a set of sparse coefficients, and then, a CSAE is established to learn the mapping relation between them. Since the proposed method leverages the feature representation ability of both sparse decomposition and CSAE, the mapping relation between the LR and HR images can be accurately obtained. Experimentally, the proposed method is compared with several state-of-the-art image SR methods on three real-world remote sensing image datasets with different spatial resolutions. The extensive experimental results demonstrate that the proposed method has gained solid improvements in terms of average peak signal-to-noise ratio and structural similarity measurement on all of the three datasets. Moreover, results also show that with larger upscaling factors, the proposed method achieves more prominent performance than the other competitive methods.
30 Oct 2019
TL;DR: It is found that sparse representation has considerably improved estimation accuracy and robustness to noise and corruption compared with least squares methods, and is a promising framework for extracting useful information from complex flow fields with realistic measurements.
Abstract: Estimating the structure of a flow field from limited and uncertain measurement data is made challenging by the richness of structures in a complex flow. An investigation of sparse representation in a library of examples shows that this can provide robust, accurate reconstructions.
TL;DR: DeepDenoiser as discussed by the authors uses a deep neural network to simultaneously learn a sparse representation of data in the time-frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise.
Abstract: Frequency filtering is widely used in routine processing of seismic data to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper, we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to simultaneously learn a sparse representation of data in the time–frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any non-seismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Because the noise statistics are automatically learned from data and require no assumptions, our method properly handles white noise, a variety of colored noise, and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in the presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that the potential applications of our approach are not limited to these applications or even to earthquake data and that our approach can be adapted to diverse signals and applications in other settings.
TL;DR: A modified adaptive orthogonal matching pursuit algorithm which estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations to improve recognition accuracy and efficiency comparing with other greedy algorithms.
Abstract: Aiming at the disadvantages of greedy algorithms in sparse solution, a modified adaptive orthogonal matching pursuit algorithm (MAOMP) is proposed in this paper. It is obviously improved to introduce sparsity and variable step size for the MAOMP. The algorithm estimates the initial value of sparsity by matching test, and will decrease the number of subsequent iterations. Finally, the step size is adjusted to select atoms and approximate the true sparsity at different stages. The simulation results show that the algorithm which has proposed improves the recognition accuracy and efficiency comparing with other greedy algorithms.
TL;DR: In this work, it is proved that the algorithm proposed in [6] approximates local minimizers of an unconstrained $\ell^0$-penalized least-squares problem, which provides sufficient conditions for general convergence, rate of convergence, and conditions for one-step recovery.
Abstract: One way to understand time-series data is to identify the underlying dynamical system which generates it. This task can be done by selecting an appropriate model and a set of parameters which best ...
TL;DR: An improved sparse representation model, namely information entropy constrained trajectory representation method (IECTR), is developed for pedestrian trajectory classification and aims to reduce the entropy for trajectory representation and to obtain superior analyzing results.
Abstract: Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse representation model, namely information entropy constrained trajectory representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of Lp-minimization (0
TL;DR: The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process.
Abstract: In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: the sparse representation-based intuitionistic fuzzy clustering-exactly precision algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity of their intuitionistic fuzzy assessment information. The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and the unsupervised clustering method and presents more robust and efficient for LSDM problems.
17 Jul 2019
TL;DR: This paper utilizes the low-rank and sparse decomposition scheme and proposes a novel Partial Multi-label Learning by Low-Rank and Sparse decomposition (PML-LRS) approach, which reformulates the observed label set into a label matrix, and decomposes it into a groundtruth label matrix and an irrelevant label matrix.
Abstract: Multi-Label Learning (MLL) aims to learn from the training data where each example is represented by a single instance while associated with a set of candidate labels. Most existing MLL methods are typically designed to handle the problem of missing labels. However, in many real-world scenarios, the labeling information for multi-label data is always redundant , which can not be solved by classical MLL methods, thus a novel Partial Multi-label Learning (PML) framework is proposed to cope with such problem, i.e. removing the the noisy labels from the multi-label sets. In this paper, in order to further improve the denoising capability of PML framework, we utilize the low-rank and sparse decomposition scheme and propose a novel Partial Multi-label Learning by Low-Rank and Sparse decomposition (PML-LRS) approach. Specifically, we first reformulate the observed label set into a label matrix, and then decompose it into a groundtruth label matrix and an irrelevant label matrix, where the former is constrained to be low rank and the latter is assumed to be sparse. Next, we utilize the feature mapping matrix to explore the label correlations and meanwhile constrain the feature mapping matrix to be low rank to prevent the proposed method from being overfitting. Finally, we obtain the ground-truth labels via minimizing the label loss, where the Augmented Lagrange Multiplier (ALM) algorithm is incorporated to solve the optimization problem. Enormous experimental results demonstrate that PML-LRS can achieve superior or competitive performance against other state-of-the-art methods.
TL;DR: A spatial–spectral hyperspectral image classification method based on multiscale superpixels and guided filter (MSS–GF) that improves the classification accuracy and the class label of each pixel is determined by majority voting rule.
Abstract: We propose a spatial–spectral hyperspectral image classification method based on multiscale superpixels and guided filter (MSS–GF). In order to use spatial information effectively, MSSs are used to get local information from different region scales. Sparse representation classifier is used to generate classification maps for each region scale. Then, multiple binary probability maps are obtained for each of the classification maps. Adding GF denoises the classification results and then improves the classification accuracy. Finally, the class label of each pixel is determined by majority voting rule.
TL;DR: Simulation results show that the proposed algorithm offers comparative performance in terms of the final network size and generalization ability, compared with state-of-the-art methods.
Abstract: The feed-forward neural network (FNN) has drawn great interest in many applications due to its universal approximation capability. In this paper, a novel algorithm for training FNNs is proposed using the concept of sparse representation. The major advantage of the proposed algorithm is that it is capable of training the initial network and optimizing the network structure simultaneously. The proposed algorithm consists of two core stages: structure optimization and weight update. In the structure optimization stage, the sparse representation technique is employed to select important hidden neurons that minimize the residual output error. In the weight update stage, a dictionary learning based method is implemented to update network weights by maximizing the output diversity from hidden neurons. This weight-updating process is designed to improve the performance of the structure optimization. Based on several benchmark classification and regression problems, we present experimental results comparing the proposed algorithm with state-of-the-art methods. Simulation results show that the proposed algorithm offers comparative performance in terms of the final network size and generalization ability.
TL;DR: A two-layer Convolutional Neural Network is proposed to learn the high-level features which utilizes to the face identification via sparse representation via a precisely selected feature exactor to outperforms other methods on given datasets.
TL;DR: A novel sparsity-based algorithm for anomaly detection in hyperspectral imagery based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood.
Abstract: In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test pixel. First, the proposed method utilizes the redundant background information to automatically remove anomalies from the background dictionary. Then, the reconstruction error obtained by the new background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the background even when some anomaly pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.