A Deep Matrix Factorization Method for Learning Attribute Representations

By George Trigeorgis, Konstantinos Bousmalis, Stefanos P. Zafeiriou, Björn Schuller. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

338 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/11f79eb93c67f6d3c2a4466161ac83c016339179

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