Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine

@inproceedings{Nguyen2013LearningPR,
  title={Learning Parts-based Representations with Nonnegative Restricted Boltzmann Machine},
  author={Tu Dinh Nguyen and Truyen Tran and Dinh Q. Phung and Svetha Venkatesh},
  booktitle={ACML},
  year={2013}
}
The success of any machine learning system depends critically on effective representations of data. In many cases, especially those in vision, it is desirable that a representation scheme uncovers the parts-based, additive nature of the data. Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings. However, when it comes to parts-based discovery, RBMs do not usually produce satisfactory results. We enhance such… CONTINUE READING
19 Citations
19 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 19 extracted citations

Similar Papers

Loading similar papers…