PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate low-rank factorization with structured factors
I Markovsky and M Niranjan
Computational Statistics and Data Analysis 2009.

Abstract

An approximate rank revealing factorization problem with structure constraints on the normalized factors is considered. Examples of structure, motivated by an application in microarray data analysis, are sparsity, nonnegativity, periodicity, and smoothness. In general, the approximate rank revealing factorization problem is nonconvex. An alternating projections algorithm is developed, which is globally convergent to a locally optimal solution. Although the algorithm is developed for a specific application in microarray data analysis, the approach is applicable to other types of structures.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5718
Deposited By:Ivan Markovsky
Deposited On:08 March 2010