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A Deep Matrix Factorization Method for Learning Attribute Representations
Trigeorgis George
Bousmalis Konstantinos
Zafeiriou Stefanos
Schuller Bjorn W
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(3): 417-429.
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.
Semi-NMF; deep semi-NMF; unsupervised feature learning; face clustering; semi-supervised learning; Deep WSF; WSF; matrix factorization; face classification
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