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A Deep Semi-NMF Model for Learning Hidden Representations
Authors: George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou and Bjoern Schuller
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
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.
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