Low-rank representation with adaptive graph regularization

DOI10.1016/j.neunet.2018.08.007zbMath1434.68472DBLPjournals/nn/WenFXTF18OpenAlexW2886265585WikidataQ91260579 ScholiaQ91260579MaRDI QIDQ2182884

Xiaozhao Fang, Yong Xu, Lunke Fei, Jie Wen, Chunwei Tian

Publication date: 26 May 2020

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2018.08.007

zbMATH Keywords

data clusteringlow-rank representationgraph regularizationrank constraint

Mathematics Subject Classification ID

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

Related Items (5)

Nonnegative self-representation with a fixed rank constraint for subspace clustering ⋮ Consensus graph learning for auto-weighted multi-view projection clustering ⋮ Beyond global fusion: a group-aware fusion approach for multi-view image clustering ⋮ Semi-supervised classification via simultaneous label and discriminant embedding estimation ⋮ MGAT: multi-view graph attention networks

Uses Software

• LFW

Cites Work

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• Inter-class sparsity based discriminative least square regression
• Joint sparse principal component analysis
• Fast singular value thresholding without singular value decomposition
• A Survey of Recent Advances in Hierarchical Clustering Algorithms
• Enhancing Low-Rank Subspace Clustering by Manifold Regularization
• Low-Rank Matrix Factorization With Adaptive Graph Regularizer
• Discriminative Elastic-Net Regularized Linear Regression

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