Tag recommendation via robust probabilistic discriminative matrix factorization

Low-rank matrix factorization serves as a key technique in learning latent factor models for many applications in machine learning. However, in many applications, observed data often exhibits different levels of noise. To address this issue, we propose a Robust Probabilistic Discriminative Matrix Factorization (RPDMF) method for binary matrix factorization on noise polluted data. We illustrate the benefits of our approach in real examples, and show how our method significantly outperforms Probabilistic Discriminative Matrix Factorization (PDMF) and classical method Weighted Nonnegative Matrix Factorization (WNMF) in the application of image tag completion.