Low-rank local tangent space embedding for subspace clustering

DOI10.1016/j.ins.2019.08.060zbMath1456.62119OpenAlexW2971342653MaRDI QIDQ1999084

Dongsheng Ye, Lvnan Xiong, Hamido Fujita, Rong Ma, Ting-Quan Deng

Publication date: 18 March 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.08.060

zbMATH Keywords

low-rank representationmanifold learningsubspace clusteringlocally linear embeddinglocal tangent space

Mathematics Subject Classification ID

Statistics on manifolds (62R30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of big data and data science (62R07)

Related Items (11)

Robust subspace clustering based on non-convex low-rank approximation and adaptive kernel ⋮ LGSLRR: towards fusing discriminative ordinal local and global structured low-rank representation for image recognition ⋮ Beyond linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms ⋮ Robust latent nonnegative matrix factorization with automatic sparse reconstruction for unsupervised feature extraction ⋮ Robust subspace clustering based on automatic weighted multiple kernel learning ⋮ Structure preserving projections learning via low-rank embedding for image classification ⋮ Learning deep discriminative representations with pseudo supervision for image clustering ⋮ Subspace clustering via joint \(\ell_{1, 2}\) and \(\ell_{2, 1}\) norms ⋮ Target redirected regression with dynamic neighborhood structure ⋮ Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering ⋮ A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF

Cites Work

• Regression analysis of locality preserving projections via sparse penalty
• Multi-view clustering based on graph-regularized nonnegative matrix factorization for object recognition
• Robust low-rank kernel multi-view subspace clustering based on the Schatten \(p\)-norm and correntropy
• Deep low-rank subspace ensemble for multi-view clustering
• Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering
• Efficient registration of multi-view point sets by K-means clustering
• Efficient \(k\)-anonymous microaggregation of multivariate numerical data via principal component analysis
• Robust Kernel Principal Component Analysis
• Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

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