Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection

Tao, D; Li, X; Wu, X; Maybank, S

Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection

Tao, D

Li, X Wu, X Maybank, S

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2008, 71 (10-12), pp. 1866 - 1882
Issue Date:: 2008-06-01

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Field	Value	Language
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Maybank, S	en_US
dc.date.issued	2008-06-01	en_US
dc.identifier.citation	Neurocomputing, 2008, 71 (10-12), pp. 1866 - 1882	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15086
dc.description.abstract	This paper proposes Tensor Rank One Discriminant Analysis (TR1DA) in which general tensors are input for pattern classification. TR1DA is based on Differential Scatter Discriminant Criterion (DSDC) and Tensor Rank One Analysis (TR1A). DSDC is a generalization of the Fisher discriminant criterion. It ensures convergence during training stage. TR1A is a method for adapting general tensors as input to DSDC. The benefits of TR1DA include: (1) a natural way of representing data without losing structure information, i.e., the information about the relative positions of pixels or regions; (2) a reduction in the small sample size problem which occurs in conventional discriminant learning because the number of training samples is much less than the dimensionality of the feature space; (3) a better convergence during the training procedure. We use a graph-embedding framework to generalize TR1DA in manifold learning-based feature selection algorithms, such as locally linear embedding, ISOMAP, and the Laplace eigenmap. We also kernelize TR1DA to nonlinear problems. TR1DA is then demonstrated to outperform traditional subspace methods, such as principal component analysis and linear discriminant analysis. © 2008 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2007.08.036	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection	en_US
dc.type	Journal Article
utslib.citation.volume	10-12	en_US
utslib.citation.volume	71	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	10-12	en_US
pubs.publication-status	Published	en_US
pubs.volume	71	en_US

Abstract:

This paper proposes Tensor Rank One Discriminant Analysis (TR1DA) in which general tensors are input for pattern classification. TR1DA is based on Differential Scatter Discriminant Criterion (DSDC) and Tensor Rank One Analysis (TR1A). DSDC is a generalization of the Fisher discriminant criterion. It ensures convergence during training stage. TR1A is a method for adapting general tensors as input to DSDC. The benefits of TR1DA include: (1) a natural way of representing data without losing structure information, i.e., the information about the relative positions of pixels or regions; (2) a reduction in the small sample size problem which occurs in conventional discriminant learning because the number of training samples is much less than the dimensionality of the feature space; (3) a better convergence during the training procedure. We use a graph-embedding framework to generalize TR1DA in manifold learning-based feature selection algorithms, such as locally linear embedding, ISOMAP, and the Laplace eigenmap. We also kernelize TR1DA to nonlinear problems. TR1DA is then demonstrated to outperform traditional subspace methods, such as principal component analysis and linear discriminant analysis. © 2008 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15086