Search Machine Learning Repository: @inproceedings{icml2014c2_trigeorgis14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {A Deep Semi-NMF Model for Learning Hidden Representations},
    Url = {http://jmlr.org/proceedings/papers/v32/trigeorgis14.pdf},
    Abstract = {Semi-NMF is a matrix factorization technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original features contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants.},
    Author = {George Trigeorgis and Konstantinos Bousmalis and Stefanos Zafeiriou and Bjoern Schuller},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1692-1700}
   }