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Linear discriminant analysis

About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.


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Proceedings ArticleDOI
10 Oct 2004
TL;DR: A systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions reports results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques.
Abstract: We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the facial action coding system (FACS), as well as recognition of fall facial expressions. Each video-frame is first scanned in real-time to detect approximately upright frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training support vector machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for recognition of full facial expressions in a 7-way forced choice was 93% correct, the best performance reported so far on the Cohn-Kanade FACS-coded expression dataset. We also applied the system to fully automated facial action coding. The present system classifies 18 action units, whether they occur singly or in combination with other actions, with a mean agreement rate of 94.5% with human FACS codes in the Cohn-Kanade dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics.

179 citations

Journal ArticleDOI
12 Aug 2008
TL;DR: The design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task is reported.
Abstract: We report the design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density (128-channel) electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task. Image clips were selected from a broad area image and presented in rapid succession (12/s) in 4.1-s bursts. Participants indicated by subsequent button press whether or not each burst of images included a target airplane feature. Image clip creation and search path selection were designed to maximize user comfort and maintain user awareness of spatial context. Independent component analysis (ICA) was used to extract a set of independent source time-courses and their minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from 128-channel EEG data recorded during clip burst presentations in a training session. The naive Bayes fusion of two Fisher discriminant classifiers, computed from the 100 most discriminative time and time-frequency features, respectively, was used to estimate the likelihood that each clip contained a target feature. This estimator was applied online in a subsequent test session. Across eight training/test session pairs from seven participants, median area under the receiver operator characteristic curve, by tenfold cross validation, was 0.97 for within-session and 0.87 for between-session estimates, and was nearly as high (0.83) for targets presented in bursts that participants mistakenly reported to include no target features.

178 citations

Journal ArticleDOI
TL;DR: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation, providing clear evidence that algorithm evaluation should be performed on several data sets using a consistent cross- validation procedure in order for the conclusions to be statistically sound.
Abstract: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound.

178 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases, and developed two more improved multiclass NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively.
Abstract: In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.

178 citations

Journal ArticleDOI
TL;DR: Results indicate that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

178 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023756
20221,711
2021678
2020815