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.
- Accepted Version
Other (Matlab files)
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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.
|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
|Date Deposited:||01 Jun 2009 14:12|
|Last Modified:||03 Jul 2012 16:21|
|Contributors:||Markovsky, Ivan (Author)
Niranjan, Mahesan (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||3|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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