interpretable nonnegative matrix decompositions
A matrix decomposition expresses a matrix as a product of at least two factor matrices. Equivalently, it expresses each column of the input matrix as a linear combination of the columns in the first factor matrix. The interpretability of the decompositions is a key issue in many data-analysis tasks. We propose two new matrix-decomposition problems: the nonnegative CX and nonnegative CUR problems, that give naturally interpretable factors. They extend the recently-proposed column and column-row based decompositions, and are aimed to be used with nonnegative matrices. Our decompositions represent the input matrix as a nonnegative linear combination of a subset of its columns (or columns and rows). We present two algorithms to solve these problems and provide an extensive experimental evaluation where we assess the quality of our algorithms' results as well as the intuitiveness of nonnegative CX and CUR decompositions. We show that our algorithms return intuitive answers with smaller reconstruction errors than the previously-proposed methods for column and column-row decompositions.
matrix decompositions column-row decompositions local search alternating least squares