eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.
Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
It is well known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms. Many NMF algorithms are sensitive with respect to the initialization of W or H or both. This is especially true of algorithms of the alternating least squares (ALS) type, including the two new ALS algorithms that we present in this paper. We compare the results of six initialization procedures (two standard and four new) on our ALS algorithms. Lastly, we discuss the practical issue of choosing an appropriate convergence criterion.
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
An Exterior Method for Nonnegative Matrix Factorization
eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.