Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features
This paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting. First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation. In particular, the weights of edges in the grap...
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Détails bibliographiques
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 11 vom: 02. Nov., Seite 3717-28
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| Auteur principal: |
Zhuang, Liansheng
(Auteur) |
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| Autres auteurs: |
Gao, Shenghua,
Tang, Jinhui,
Wang, Jingjing,
Lin, Zhouchen,
Ma, Yi,
Yu, Nenghai |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2015
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Sujets: | Journal Article
Research Support, Non-U.S. Gov't |
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