Descent methods for Nonnegative Matrix Factorization

Details Descent methods for Nonnegative Matrix Factorization Descent methods for Nonnegative Matrix Factorization

2008-01-21

arxiv.org/abs/0801.3199v3

Computer Science

Numerical Analysis

47 pages. New convergence proof using damped version of RRI. To appear in Numerical Linear Algebra in Signals, Systems and Con

Scientific paper

In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method called Rank-one Residue Iteration (RRI). We also give a comparison of these different methods and show that the new block coordinate method has better properties in terms of approximation error and complexity. By interpreting this method as a rank-one approximation of the residue matrix, we prove that it \emph{converges} and also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.

Affiliated with

Also associated with

No associations

Rating

Descent methods for Nonnegative Matrix Factorization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Descent methods for Nonnegative Matrix Factorization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Descent methods for Nonnegative Matrix Factorization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-594232