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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2554555
|2 doi
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|a eng
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| 100 |
1 |
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|a Trigeorgis, George
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A Deep Matrix Factorization Method for Learning Attribute Representations
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|c 2017
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 20.09.2018
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|a Date Revised 20.09.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Bousmalis, Konstantinos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zafeiriou, Stefanos
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schuller, Bjorn W
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 3 vom: 15. März, Seite 417-429
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|g volume:39
|g year:2017
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|g day:15
|g month:03
|g pages:417-429
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|u http://dx.doi.org/10.1109/TPAMI.2016.2554555
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