Approximate low-rank factorization with structured factors


Markovsky, Ivan and Niranjan, Mahesan (2010) Approximate low-rank factorization with structured factors Computational Statistics & Data Analysis, 54, pp. 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
ISSNs: 0167-9473 (print)
Keywords: rank revealing factorization, numerical rank, low-rank approximation, maximum likelihood PCA, total least squares, errors-in-variables, microarray data.
Organisations: Southampton Wireless Group
ePrint ID: 267440
Date :
Date Event
August 2010Published
Date Deposited: 01 Jun 2009 14:12
Last Modified: 16 Jun 2017 16:41
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/267440

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