The University of Southampton
University of Southampton Institutional Repository

Approximate low-rank factorization with structured factors

Approximate low-rank factorization with structured factors
Approximate low-rank factorization with structured factors
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.
rank revealing factorization, numerical rank, low-rank approximation, maximum likelihood PCA, total least squares, errors-in-variables, microarray data.
0167-9473
3411-3420
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

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

Record type: Article

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.

Text
factorize_rev.pdf - Accepted Manuscript
Download (117kB)
Archive
factorize.tar - Other
Download (20kB)

More information

Published date: August 2010
Keywords: rank revealing factorization, numerical rank, low-rank approximation, maximum likelihood PCA, total least squares, errors-in-variables, microarray data.
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267440
URI: http://eprints.soton.ac.uk/id/eprint/267440
ISSN: 0167-9473
PURE UUID: 2c9e82f5-392a-4b4e-be3f-11304c01015b
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 01 Jun 2009 14:12
Last modified: 15 Mar 2024 03:29

Export record

Contributors

Author: Ivan Markovsky
Author: Mahesan Niranjan ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×