Linear and Nonlinear Projective Nonnegative Matrix Factorization

Abstract

A variant of nonnegative matrix factorization (NMF) which was proposed earlier is analyzed here. It is called projective nonnegative matrix factorization (PNMF). The new method approximately factorizes a projection matrix, minimizing the reconstruction error, into a positive low-rank matrix and its transpose. The dissimilarity between the original data matrix and its approximation can be measured by the Frobenius matrix norm or the modified Kullback-Leibler divergence. Both measures are minimized by multiplicative update rules, whose convergence is proven for the first time. Enforcing orthonormality to the basic objective is shown to lead to an even more efficient update rule, which is also readily extended to nonlinear cases. The formulation of the PNMF objective is shown to be connected to a variety of existing NMF methods and clustering approaches. In addition, the derivation using Lagrangian multipliers reveals the relation between reconstruction and sparseness. For kernel principal component analysis (PCA) with the binary constraint, useful in graph partitioning problems, the nonlinear kernel PNMF provides a good approximation which outperforms an existing discretization approach. Empirical study on three real-world databases shows that PNMF can achieve the best or close to the best in clustering. The proposed algorithm runs more efficiently than the compared NMF methods, especially for high-dimensional data. Moreover, contrary to the basic NMF, the trained projection matrix can be readily used for newly coming samples and demonstrates good generalization.