Non-negative patch alignment framework

Guan, N; Tao, D; Luo, Z; Yuan, B

Non-negative patch alignment framework

Guan, N Tao, D

Luo, Z Yuan, B

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks, 2011, 22 (8), pp. 1218 - 1230
Issue Date:: 2011-08-01

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Field	Value	Language
dc.contributor.author	Guan, N	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Luo, Z	en_US
dc.contributor.author	Yuan, B	en_US
dc.date.issued	2011-08-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks, 2011, 22 (8), pp. 1218 - 1230	en_US
dc.identifier.issn	1045-9227	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18249
dc.description.abstract	In this paper, we present a non-negative patch alignment framework (NPAF) to unify popular non-negative matrix factorization (NMF) related dimension reduction algorithms. It offers a new viewpoint to better understand the common property of different NMF algorithms. Although multiplicative update rule (MUR) can solve NPAF and is easy to implement, it converges slowly. Thus, we propose a fast gradient descent (FGD) to overcome the aforementioned problem. FGD uses the Newton method to search the optimal step size, and thus converges faster than MUR. Experiments on synthetic and real-world datasets confirm the efficiency of FGD compared with MUR for optimizing NPAF. Based on NPAF, we develop non-negative discriminative locality alignment (NDLA). Experiments on face image and handwritten datasets suggest the effectiveness of NDLA in classification tasks and its robustness to image occlusions, compared with representative NMF-related dimension reduction algorithms. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks	en_US
dc.relation.isbasedon	10.1109/TNN.2011.2157359	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Non-negative patch alignment framework	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	22	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	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	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

In this paper, we present a non-negative patch alignment framework (NPAF) to unify popular non-negative matrix factorization (NMF) related dimension reduction algorithms. It offers a new viewpoint to better understand the common property of different NMF algorithms. Although multiplicative update rule (MUR) can solve NPAF and is easy to implement, it converges slowly. Thus, we propose a fast gradient descent (FGD) to overcome the aforementioned problem. FGD uses the Newton method to search the optimal step size, and thus converges faster than MUR. Experiments on synthetic and real-world datasets confirm the efficiency of FGD compared with MUR for optimizing NPAF. Based on NPAF, we develop non-negative discriminative locality alignment (NDLA). Experiments on face image and handwritten datasets suggest the effectiveness of NDLA in classification tasks and its robustness to image occlusions, compared with representative NMF-related dimension reduction algorithms. © 2011 IEEE.

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http://hdl.handle.net/10453/18249