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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as [Image: see text], where d(k), u(k), and v(k) minimize the squared Frobenius norm of X[Image: see text], subject to penalties on u(k) and v(k). This re...
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| Hlavní autoři: | , , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
Oxford University Press
2009
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| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2697346/ https://ncbi.nlm.nih.gov/pubmed/19377034 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/biostatistics/kxp008 |
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