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.
The singular value decomposition of a matrix is used to derive systematically the Moore-Penrose inverse for a matrix bordered by a row and a column, in addition to the Moore-Penrose inverse for the ...
Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the technique that emphasizes simplicity and ease-of-modification over robustness and ...
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