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2005
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generalization error bounds for predicting unobserved entries that are based on these measures. We also consider the possible relations between these measures.
Nathan Srebro, Adi Shraibman
openaire +1 more source
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generalization error bounds for predicting unobserved entries that are based on these measures. We also consider the possible relations between these measures.
Nathan Srebro, Adi Shraibman
openaire +1 more source
Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020Canyi Lu, Jiashi Feng, Yudong Chen
exaly
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Nojun Kwak
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Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Yao Hu, Debing Zhang, Jieping Ye
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