Biclustering via sparse singular value decomposition

Biometrics, Volume 66, Issue 4, 2010, Pages 1087-1095.

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Keywords:
dimension reductionbiclusteringsingular value decompositionprincipal component analysis

Abstract:

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forci...More

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