Tag recommendation via robust probabilistic discriminative matrix factorization

Cheng Lu; Bin Shen; Lu Zhang; Jan P. Allebach

ICASSP 2016accepted0 citations

Tag recommendation via robust probabilistic discriminative matrix factorization

Cheng Lu, Bin Shen, Lu Zhang, Jan P. Allebach

Abstract

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.

BibTeX

@inproceedings{icassp2016_tagrecommendatio,
  title = {Tag recommendation via robust probabilistic discriminative matrix factorization},
  author = {Cheng Lu and Bin Shen and Lu Zhang and Jan P. Allebach},
  booktitle = {ICASSP 2016},
  year = {2016}
}