Diversified Bayesian Nonnegative Matrix Factorization


  • Qiao Maoying The Commonwealth Scientific and Industrial Research Organisation
  • Yu Jun Hangzhou Dianzi University
  • Liu Tongliang The University of Sydney
  • Wang Xinchao Stevens Institute of Technology
  • Tao Dacheng The University of Sydney




Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data. In this paper, we approach this issue using a Bayesian framework. We propose to assign a diversity prior to the parts of the factorization to induce correctness based on the assumption that useful parts should be distinct and thus well-spread. A Bayesian framework including this diversity prior is then established. This framework aims at inducing factorizations embracing both good data fitness from maximizing likelihood and large separability from the diversity prior. Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. To carry out the inference, a Monte Carlo Markov Chain (MCMC) based procedure is derived. Experiments conducted on a synthetic dataset and a real-world MULAN dataset for multi-label learning (MLL) task demonstrate the superiority of the proposed method.




How to Cite

Maoying, Q., Jun, Y., Tongliang, L., Xinchao, W., & Dacheng, T. (2020). Diversified Bayesian Nonnegative Matrix Factorization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5420-5427. https://doi.org/10.1609/aaai.v34i04.5991



AAAI Technical Track: Machine Learning