Identification of NMF by choosing maximum-volume basis vectors
This addresses limitations in NMF for data analysis where sparsity does not hold, offering a novel approach for improved interpretability and performance in such scenarios.
The paper tackles the problem of nonnegative matrix factorization (NMF) failing for highly mixed data due to sparsity assumptions, proposing a maximum-volume-constrained NMF framework to make basis vectors distinct, with experimental results showing effectiveness.
In nonnegative matrix factorization (NMF), minimum-volume-constrained NMF is a widely used framework for identifying the solution of NMF by making basis vectors as similar as possible. This typically induces sparsity in the coefficient matrix, with each row containing zero entries. Consequently, minimum-volume-constrained NMF may fail for highly mixed data, where such sparsity does not hold. Moreover, the estimated basis vectors in minimum-volume-constrained NMF may be difficult to interpret as they may be mixtures of the ground truth basis vectors. To address these limitations, in this paper we propose a new NMF framework, called maximum-volume-constrained NMF, which makes the basis vectors as distinct as possible. We further establish an identifiability theorem for maximum-volume-constrained NMF and provide an algorithm to estimate it. Experimental results demonstrate the effectiveness of the proposed method.