NMF variant learns local similarity and clustering mutually to yield representations that better reveal data geometry, with nonlinear extension and convergent multiplicative updates.
Low-rank matrix recovery via efficient schatten p-norm minimization
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
An iteratively re-weighted method (IRW) is introduced for optimization problems with sparsity-inducing norms, supported by a convergence guarantee and shown to outperform alternatives on robust feature selection.
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Nonnegative Matrix Factorization with Local Similarity Learning
NMF variant learns local similarity and clustering mutually to yield representations that better reveal data geometry, with nonlinear extension and convergent multiplicative updates.
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An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
An iteratively re-weighted method (IRW) is introduced for optimization problems with sparsity-inducing norms, supported by a convergence guarantee and shown to outperform alternatives on robust feature selection.