Nonnegative discriminative manifold learning for hyperspectral data dimension reduction

Zhang, L; Tao, D; Huang, X; Du, B

Nonnegative discriminative manifold learning for hyperspectral data dimension reduction

Zhang, L Tao, D

Huang, X Du, B

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8261 LNCS pp. 351 - 358
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Du, B	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8261 LNCS pp. 351 - 358	en_US
dc.identifier.isbn	9783642420566	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120833
dc.description.abstract	Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms could not preserve the nonnegative property of the hyperspectral data, which leads inconsistency with the psychological intuition of "combining parts to form a whole". In this paper, we introduce a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-42057-3-45	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Nonnegative discriminative manifold learning for hyperspectral data dimension reduction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8261 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US
pubs.volume	8261 LNCS	en_US

Abstract:

Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms could not preserve the nonnegative property of the hyperspectral data, which leads inconsistency with the psychological intuition of "combining parts to form a whole". In this paper, we introduce a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods. © 2013 Springer-Verlag Berlin Heidelberg.

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