Sciweavers

CSDA
2010

Approximate low-rank factorization with structured factors

13 years 3 months ago
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. Key words: rank revealing factorization; numerical rank; low-rank approximation; maximum likelihood PCA; total least squares; errors-in-variables; microarray data.
Ivan Markovsky, Mahesan Niranjan
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CSDA
Authors Ivan Markovsky, Mahesan Niranjan
Comments (0)