Nonnegative discriminative manifold learning for hyperspectral data dimension reduction
© 2013 IEEE. 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 can not preserve the nonnegativity property of the hyperspectral...
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ftunivtsydney:oai:opus.lib.uts.edu.au:10453/127225 2023-05-15T16:01:51+02:00 Nonnegative discriminative manifold learning for hyperspectral data dimension reduction Zhang, L Tao, D Huang, X Xia, GS 2013-06-28 application/pdf http://hdl.handle.net/10453/127225 unknown Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing 10.1109/WHISPERS.2013.8080702 Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2013, 2013-June 9781509011193 2158-6276 http://hdl.handle.net/10453/127225 Conference Proceeding 2013 ftunivtsydney 2022-03-13T13:56:14Z © 2013 IEEE. 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 can not preserve the nonnegativity property of the hyperspectral data, which leads inconsistency with the psychological intuition of 'combining parts to form a whole'. In this paper, we propose 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. Conference Object DML University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
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University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
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© 2013 IEEE. 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 can not preserve the nonnegativity property of the hyperspectral data, which leads inconsistency with the psychological intuition of 'combining parts to form a whole'. In this paper, we propose 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. |
format |
Conference Object |
author |
Zhang, L Tao, D Huang, X Xia, GS |
spellingShingle |
Zhang, L Tao, D Huang, X Xia, GS Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
author_facet |
Zhang, L Tao, D Huang, X Xia, GS |
author_sort |
Zhang, L |
title |
Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
title_short |
Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
title_full |
Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
title_fullStr |
Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
title_full_unstemmed |
Nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
title_sort |
nonnegative discriminative manifold learning for hyperspectral data dimension reduction |
publishDate |
2013 |
url |
http://hdl.handle.net/10453/127225 |
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DML |
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DML |
op_relation |
Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing 10.1109/WHISPERS.2013.8080702 Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2013, 2013-June 9781509011193 2158-6276 http://hdl.handle.net/10453/127225 |
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1766397551759589376 |