Active Subspace: Toward Scalable Low-Rank Learning

DOI10.1162/NECO_a_00369zbMath1268.68139WikidataQ50785539 ScholiaQ50785539MaRDI QIDQ2840899

Shuicheng Yan, Guangcan Liu

Publication date: 23 July 2013

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

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Scalable Low-Rank Representation ⋮ Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning ⋮ Parallel active subspace decomposition for tensor robust principal component analysis ⋮ Robust alternating low-rank representation by joint \(L_p\)- and \(L_{2,p}\)-norm minimization ⋮ Dynamic behavior analysis via structured rank minimization ⋮ Fast Estimation of Approximate Matrix Ranks Using Spectral Densities ⋮ Robust bilinear factorization with missing and grossly corrupted observations ⋮ Similarity preserving low-rank representation for enhanced data representation and effective subspace learning

Uses Software

• LMaFit

Cites Work

• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• TILT: transform invariant low-rank textures
• Local minima and convergence in low-rank semidefinite programming
• Fast singular value thresholding without singular value decomposition
• Exact matrix completion via convex optimization
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Rank-Sparsity Incoherence for Matrix Decomposition
• The Geometry of Algorithms with Orthogonality Constraints

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