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Approximate low-rank factorization with structured factors

Markovsky, Ivan and Niranjan, Mahesan (2010) Approximate low-rank factorization with structured factors. Computational Statistics and Data Analysis, 54, 3411-3420.

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Description/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 structure.

Item Type:Article
Uncontrolled Keywords:rank revealing factorization; numerical rank; low-rank approximation; maximum likelihood PCA; total least squares; errors-in-variables; microarray data.
Divisions:Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID:267440
Deposited On:01 Jun 2009 15:12
Last Modified:01 Mar 2012 16:39
Further Information:Google Scholar

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