Low Rank Tensor Manifold Learning

DOI10.1007/978-3-319-12000-3_7zbMath1323.68455OpenAlexW100649091MaRDI QIDQ3449322

Mohamed Cheriet, Guoqiang Zhong

Publication date: 4 November 2015

Published in: Low-Rank and Sparse Modeling for Visual Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-12000-3_7

zbMATH Keywords

tensordimensionality reductionperceptionmanifold learninglow rank

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Machine vision and scene understanding (68T45)

Uses Software

• darch
• t-SNE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Tensor Decompositions and Applications
• Fixed point and Bregman iterative methods for matrix rank minimization
• Sparse non-negative tensor factorization using columnwise coordinate descent
• Exact matrix completion via convex optimization
• Reducing the Dimensionality of Data with Neural Networks
• How to Grow a Mind: Statistics, Structure, and Abstraction
• Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
• Learning the parts of objects by non-negative matrix factorization
• A Fast Learning Algorithm for Deep Belief Nets

This page was built for publication: Low Rank Tensor Manifold Learning