A General Exponential Framework for Dimensionality Reduction
As a general framework, Laplacian embedding, based on a pairwise similarity matrix, infers low dimensional representations from high dimensional data. However, it generally suffers from three issues: 1) algorithmic performance is sensitive to the size of neighbors; 2) the algorithm encounters the we...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 2 vom: 07. Feb., Seite 920-30
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| 1. Verfasser: |
Wang, Su-Jing
(VerfasserIn) |
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| Weitere Verfasser: |
Yan, Shuicheng,
Yang, Jian,
Zhou, Chun-Guang,
Fu, Xiaolan |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2014
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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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