Subspace clustering using a low-rank constrained autoencoder

DOI10.1016/J.INS.2017.09.047zbMath1436.68319OpenAlexW2754768167MaRDI QIDQ781155

D. Kharzeev

Publication date: 16 July 2020

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

Full work available at URL: https://doi.org/10.1016/j.ins.2017.09.047

zbMATH Keywords

autoencoderlow-rank representationsubspace clusteringdeep neural networks

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07)

Related Items (4)

Beyond linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms ⋮ A new distance with derivative information for functional \(k\)-means clustering algorithm ⋮ Robust low-rank kernel multi-view subspace clustering based on the Schatten \(p\)-norm and correntropy ⋮ Attributed graph clustering with subspace stochastic block model

Uses Software

• darch
• COIL-100

Cites Work

• Unnamed Item
• Reducing the Dimensionality of Data with Neural Networks
• Low-Rank Matrix Completion in the Presence of High Coherence
• Learning with $\ell ^{1}$-graph for image analysis

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