This approach is founded upon the obser- vation that if the constraints active at the final solution are known in advance, the original problem can be solved by.
The Frobenius norm is the sum of Euclidean norms over columns. Optimization over B (or C) boils down to a series of nonnegative least squares (NNLS) problems.
Dec 18, 2013 · In this paper we present new and improved algorithms for the least-squares NNMA problem, which are not only theoretically well-founded, but also ...
In this paper we present new and improved algorithms for the least-squares NNMA problem, which are theoretically well-founded and overcome many of the ...
Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. Proceedings of SIAM Conference on Data Mining, 2007. C. L. Lawson ...
Dec 28, 2007 · Broadly viewed, our method for solving NNLS may be viewed as combining the active set method with the projected gradient scheme. This approach ...
In this paper, we propose a fast NMF algorithm via Projected Newton Method (PNM). First, we propose PNM to efficiently solve a nonnegative least squares problem ...
Sep 11, 2024 · In this article, we study algorithms for nonnegative matrix factorization (NMF) in various applications involving streaming data.
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Jun 19, 2012 · Iteratively solve minW ≥0 f (W,H) and minH≥0 f (W,H) until convergence. For least squares NMF, each sub-problem can be exactly or.
This article develops a fast two-stage algorithm for highly efficient and accurate NMF, using an alternating non-negative least squares framework in ...