A deep matrix factorization method for learning attribute representations
This work addresses the need for better clustering and classification in datasets with complex hierarchical attributes, though it appears incremental as it builds on existing matrix factorization techniques.
The authors tackled the problem of learning hidden attribute representations for clustering and classification by proposing Deep Semi-NMF and its semi-supervised variant Deep WSF, which outperformed Semi-NMF and other state-of-the-art methods.
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 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 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.