title: A Deep Matrix Factorization Method for Learning Attribute Representations.
abstract: Semi-Non-negative Matrix Factorization is a 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 data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies cannot 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 also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: George
authors.initials: G
authors.lastname: Trigeorgis
authors.affiliation: Google Research, Mountain View, CA.
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Bousmalis
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: Stefanos
authors.initials: S
authors.lastname: Zafeiriou
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: Bjorn W
authors.initials: BW
authors.lastname: Schuller