Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better representation. However, current NMF methods do not always generate sparse solutions. In this paper, we propose new method log-norm imposed on the factor matrices enhance sparseness. Mor...

In this paper, we propose a general formulation for kernel nonnegative matrix factorization with flexible kernels. Specifically, we propose the Gaussian nonnegative matrix factorization (GNMF) algorithm by using the Gaussian kernel in the framework. Different from a recently developed polynomial NMF (PNMF), GNMF finds basis vectors in the kernel-induced feature space and the computational cost ...

Along with the growth of the Internet, automatic recommender systems have become popular. Due to being intuitive and useful, factorization based models, including the Nonnegative Matrix Factorization (NMF) model, are one of the most common approachs for building recommender systems. In this study, we focus on how a recommender system can be built for online services and how the parameters of an...