2008 InterpretableNonnegativeMatrixD

• (Hyvönen et al., 2008) ⇒ Saara Hyvönen, Pauli Miettinen, and Evimaria Terzi. (2008). “Interpretable Nonnegative Matrix Decompositions.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401935

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Interpretable+nonnegative+matrix+decompositions%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401935&preflayout=flat#citedby

Quotes

Abstract

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.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 InterpretableNonnegativeMatrixD	Evimaria Terzi Saara Hyvönen Pauli Miettinen			Interpretable Nonnegative Matrix Decompositions				10.1145/1401890.1401935