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...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | 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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| 1. Verfasser: |
Zhuang, Liansheng
(VerfasserIn) |
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| Weitere Verfasser: |
Gao, Shenghua,
Tang, Jinhui,
Wang, Jingjing,
Lin, Zhouchen,
Ma, Yi,
Yu, Nenghai |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2015
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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