Block-Toeplitz/Hankel Structured Total Least Squares
SIAM Journal on Matrix Analysis and Applications
The element-wise weighted total least-squares problem
Computational Statistics & Data Analysis
Convergence of the sequence of parameters generated by alternating least squares algorithms
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Editorial: 3rd Special issue on matrix computations and statistics
Computational Statistics & Data Analysis
Low-Rank Matrix Approximation with Weights or Missing Data Is NP-Hard
SIAM Journal on Matrix Analysis and Applications
Noniterative Convex Optimization Methods for Network Component Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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 structures.