Generalized transfer subspace learning through low-rank constraint

DOI10.1007/s11263-014-0696-6zbMath1328.68250OpenAlexW2157032359MaRDI QIDQ903542

Ming Shao, Yun Fu, Dmitry Kit

Publication date: 6 January 2016

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-014-0696-6

zbMATH Keywords

subspace learningtransfer learningdomain adaptationlow-rank constraint

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Machine vision and scene understanding (68T45)

Related Items (8)

Low-Rank Transfer Learning ⋮ Lp- and Ls-norm distance based robust linear discriminant analysis ⋮ Low-rank and sparse embedding for dimensionality reduction ⋮ High-performance community detection in social networks using a deep transitive autoencoder ⋮ Fisher-discriminative regularized latent sparse transfer model ⋮ Sparsity preserving discriminant projections with applications to face recognition ⋮ Robust multi-source adaptation visual classification using supervised low-rank representation ⋮ Self-regularized fixed-rank representation for subspace segmentation

Uses Software

• Algorithm 432
• Caltech-256

Cites Work

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• Matrix multiplication via arithmetic progressions
• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• Exact matrix completion via convex optimization
• A Singular Value Thresholding Algorithm for Matrix Completion
• A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Algorithm 432 [C2: Solution of the matrix equation AX + XB = C [F4]]

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