Unsupervised multi-view feature extraction with dynamic graph learning
• We devise a framework that unifies dynamic graph and feature extraction learning. • An effective optimization solution guaranteed desirable convergence is proposed. • Extensive experiments on public multi-view datasets demonstrate the proposed method.
Gespeichert in:
| Autor*in: |
Shi, Dan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Propolis as lipid bioactive nano-carrier for topical nasal drug delivery - Rassu, Giovanna ELSEVIER, 2015, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:56 ; year:2018 ; pages:256-264 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jvcir.2018.09.019 |
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• We devise a framework that unifies dynamic graph and feature extraction learning. • An effective optimization solution guaranteed desirable convergence is proposed. • Extensive experiments on public multi-view datasets demonstrate the proposed method. |
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Unsupervised multi-view feature extraction with dynamic graph learning |
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https://doi.org/10.1016/j.jvcir.2018.09.019 |
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Zhu, Lei Cheng, Zhiyong Li, Zhihui Zhang, Huaxiang |
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10.1016/j.jvcir.2018.09.019 |
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