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18
Regularized common spatial patterns with generic learning for EEG signal classification
- in Proc. EMBC
, 2009
"... Abstract—Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a smallsample setting (SSS). Conventional CSP is based on a sa ..."
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Cited by 12 (3 self)
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Abstract—Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a smallsample setting (SSS). Conventional CSP is based on a samplebased covariance matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed where the covariance matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS. Index Terms—Brain-computer interface (BCI), common spatial pattern (CSP), electroencephalogram (EEG), small sample, regularization, aggregation, generic learning.
Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization
"... Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality ..."
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Cited by 12 (7 self)
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Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality reduction algorithm for tensorial data, named as uncorrelated multilinear PCA (UMPCA). UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. We evaluate the UMPCA on a second-order tensorial problem, face recognition, and the experimental results show its superiority, especially in lowdimensional spaces, through the comparison with three other PCA-based algorithms. 1.
Face Recognition Using Adaptive Margin Fisher’s Criterion and Linear Discriminant Analysis (AMFC-LDA) IAJIT First Online Publication
, 2011
"... Abstract: Selecting a low dimensional feature subspace from thousands of features is a key phenomenon for optimal classification. Linear Discriminant Analysis (LDA) is a basic well recognized supervised classifier that is effectively employed for classification. However, two problems arise in intra ..."
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Cited by 3 (1 self)
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Abstract: Selecting a low dimensional feature subspace from thousands of features is a key phenomenon for optimal classification. Linear Discriminant Analysis (LDA) is a basic well recognized supervised classifier that is effectively employed for classification. However, two problems arise in intra class during Discriminant Analysis. Firstly, in training phase the number of samples in intra class is smaller than the dimensionality of the sample which makes LDA unstable. The other is high computational cost due to redundant and irrelevant data points in intra class. An Adaptive Margin Fisher’s Criterion Linear Discriminant Analysis (AMFC-LDA) is proposed that addresses these issues and overcomes the limitations of intra class problems. Small Sample Size problem is resolved through modified maximum margin criterion which is a form of customized LDA and Convex hull. Inter class is defined using LDA while intra class is formulated using quick hull respectively. Similarly, computational cost is reduced by reformulating within class scatter matrix through Minimum Redundancy Maximum Relevance (mRMR) algorithm while preserving Discriminant Information. The proposed algorithm reveals encouraging performance. Finally, a comparison is made with existing approaches.
FACE RECOGNITION WITH BIOMETRIC ENCRYPTION FOR PRIVACY-ENHANCING SELF-EXCLUSION
"... Face recognition has been employed in various securityrelated applications such as surveillance, mugshot identification, e-passport, and access control. Despite its recent advancements, privacy concern is one of several issues preventing its wider deployment. In this paper, we address the privacy co ..."
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Cited by 2 (0 self)
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Face recognition has been employed in various securityrelated applications such as surveillance, mugshot identification, e-passport, and access control. Despite its recent advancements, privacy concern is one of several issues preventing its wider deployment. In this paper, we address the privacy concern for a self-exclusion scenario of face recognition, through combining face recognition with a simple biometric encryption scheme called helper data system. The combined system is described in detail with focus on the key binding procedure. Experiments are carried out on the CMU PIE face database. The experimental results demonstrate that in the proposed system, the biometric encryption module tends to significantly reduce the false acceptance rate while increasing the false rejection rate. Index Terms — Face recognition, biometric encryption, security, privacy, watch list.
A Biometric Encryption System for the Self-Exclusion Scenario of Face Recognition
, 2009
"... This paper presents a biometric encryption system that addresses the privacy concern in the deployment of the face recognition technology in real-world systems. In particular, we focus on a self-exclusion scenario (a special application of watch-list) of face recognition and propose a novel design o ..."
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Cited by 1 (1 self)
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This paper presents a biometric encryption system that addresses the privacy concern in the deployment of the face recognition technology in real-world systems. In particular, we focus on a self-exclusion scenario (a special application of watch-list) of face recognition and propose a novel design of a biometric encryption system deployed with a face recognition system under constrained conditions. From a system perspective, we investigate issues ranging from image preprocessing, feature extraction, to cryptography, error-correcting coding/decoding, key binding, and bit allocation. In simulation studies, the proposed biometric encryption system is tested on the CMU PIE face database. An important observation from the simulation results is that in the proposed system, the biometric encryption module tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate. Index Terms Biometric encryption, face recognition, self exclusion, watch list, security, privacy. The work presented in this paper has been partially supported by the Ontario Lottery and Gaming Corporation (OLG). The views, opinions, and findings contained in this paper are those of the authors and should not be construed as official positions, policies, or decisions of the OLG, unless so designated by other official documentation.
Learning canonical correlations of paired tensor sets via tensor-to-vector projection
- In: the 23rd International Joint Conference on Artificial Intelligence (2013
"... Canonical correlation analysis (CCA) is a useful technique for measuring relationship between two sets of vector data. For paired tensor data sets, we propose a multilinear CCA (MCCA) method. Unlike existing multilinear variations of CCA, MCCA extracts uncorrelated features under two architectures w ..."
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Cited by 1 (1 self)
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Canonical correlation analysis (CCA) is a useful technique for measuring relationship between two sets of vector data. For paired tensor data sets, we propose a multilinear CCA (MCCA) method. Unlike existing multilinear variations of CCA, MCCA extracts uncorrelated features under two architectures while maximizing paired correlations. Through a pair of tensor-to-vector projections, one architecture enforces zero-correlation within each set while the other enforces zero-correlation between different pairs of the two sets. We take a successive and iterative approach to solve the problem. Experiments on matching faces of different poses show that MCCA outperforms CCA and 2D-CCA, while using much fewer features. In addition, the fusion of two architectures leads to performance improvement, indicating complementary information. 1
Aggregation of sparse linear discriminant analysis for event-related potential classification in brain-computer interface
- Int. J. Neural Syst
, 2014
"... Received (to be inserted ..."
EURASIP IVP/713183.V2 1 Boosting Discriminant Learners for Gait Recognition using MPCA Features
, 2009
"... This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial fea ..."
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This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into a LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge ” data sets shows that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-theart gait recognition algorithms.
doi:10.1155/2009/713183 Research Article Boosting Discriminant Learners for Gait Recognition Using MPCA Features
"... This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial fea ..."
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This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge ” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms. Copyright © 2009 Haiping Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
Generalized Multilinear Model for Dimensionality Reduction of Binary Tensors
, 2012
"... Generalized multilinear model for dimensionality reduction of binary tensors (GMM-DR-BT) is a technique for computing low-rank approximations of multi-dimensional data objects, tensors. The model exposes a latent structure that represents dominant trends in the binary tensorial data while retaining ..."
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Generalized multilinear model for dimensionality reduction of binary tensors (GMM-DR-BT) is a technique for computing low-rank approximations of multi-dimensional data objects, tensors. The model exposes a latent structure that represents dominant trends in the binary tensorial data while retaining as much information as possible. Recently, there exist several models for computing the low-rank approximations of tensors but to the best of our knowledge at present there is no principled framework for binary tensors. Although the binary tensors occur in many real world applications such as gait recognition, document analysis or graph mining. In the GMM-DR-BT model formulation, to account for binary nature of the data, each tensor element is modeled by a Bernoulli noise distribution. To extract the dominant trends in the data, the natural parameters of the Bernoulli distributions are constrained by employing the Tucker model to lie in a sub-space spanned by a reduced set of basis (principal) tensors. Bernoulli distribution is a member of exponential family with helpful analytical properties that allow us to derive an iterative scheme for estimation of the basis tensors and other model parameters via maximum likelihood. Furthermore, we extended the fully unsupervised GMM-DR-BT model to the semi-supervised setting by forcing the model to search for a natural parameter subspace that represents a user specified compromise between modelling the quality and the degree of class separation.
