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A Deep Matrix Factorization Method for Learning Attribute Representations
Open accessAbstract
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.
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Metadata
- Authors
- Trigeorgis, G., Bousmalis, K., Zafeiriou, S., & Schuller, B. W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-03-01
- Volume
- 39
- Issue
- 3
- Pages
- 417-429
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2108433027
- Cited by
- 356
- References
- 51
- FWCI
- 18.94
- Type
- journal article
- Language
- en
- Open access
- green
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