Nonnegative matrix and tensor factorizations, least squares problems, and applications

Nonnegative matrix and tensor factorizations, least squares problems, and applications

Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative value, and it provides a low-rank approximation formed by factors whose elements...

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Bibliographic Details
Main Author: Kim, Jingu
Published: Georgia Institute of Technology 2012
Subjects:
Linear complementarity problem
Parallel factorization
Canonical decomposition
Active set method
Rank deficiency
l1-regularized linear regression
Mixed-norm regularization
Low rank approximation
Block principal pivoting
Nonnegativity constrained least squares
Computer science
Matrices
Least squares
Online Access:http://hdl.handle.net/1853/42909

Internet

http://hdl.handle.net/1853/42909

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