Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering

DOI10.1016/j.neunet.2018.03.006zbMath1434.68469arXiv1708.02288OpenAlexW2963714593WikidataQ52602169 ScholiaQ52602169MaRDI QIDQ2179829

Yang Wang, Lin Wu

Publication date: 13 May 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.02288

zbMATH Keywords

clusteringlow-rank representationmulti-view subspace learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Multiview clustering via exclusive non-negative subspace learning and constraint propagation ⋮ Dual self-paced multi-view clustering ⋮ Adaptive KNN and graph-based auto-weighted multi-view consensus spectral learning ⋮ MGAT: multi-view graph attention networks

Cites Work

• Group Sparse Multiview Patch Alignment Framework With View Consistency for Image Classification
• Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus
• Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval

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