A Deep Semi-NMF Model for Learning Hidden Representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjoern Schuller
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1692-1700, 2014.

Abstract

Semi-NMF is a matrix factorization 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 features 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 show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-trigeorgis14, title = {A Deep Semi-NMF Model for Learning Hidden Representations}, author = {Trigeorgis, George and Bousmalis, Konstantinos and Zafeiriou, Stefanos and Schuller, Bjoern}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1692--1700}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/trigeorgis14.pdf}, url = {https://proceedings.mlr.press/v32/trigeorgis14.html}, abstract = {Semi-NMF is a matrix factorization 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 features 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 show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants.} }
Endnote
%0 Conference Paper %T A Deep Semi-NMF Model for Learning Hidden Representations %A George Trigeorgis %A Konstantinos Bousmalis %A Stefanos Zafeiriou %A Bjoern Schuller %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-trigeorgis14 %I PMLR %P 1692--1700 %U https://proceedings.mlr.press/v32/trigeorgis14.html %V 32 %N 2 %X Semi-NMF is a matrix factorization 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 features 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 show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants.
RIS
TY - CPAPER TI - A Deep Semi-NMF Model for Learning Hidden Representations AU - George Trigeorgis AU - Konstantinos Bousmalis AU - Stefanos Zafeiriou AU - Bjoern Schuller BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-trigeorgis14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 1692 EP - 1700 L1 - http://proceedings.mlr.press/v32/trigeorgis14.pdf UR - https://proceedings.mlr.press/v32/trigeorgis14.html AB - Semi-NMF is a matrix factorization 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 features 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 show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming Semi-NMF, but also other NMF variants. ER -
APA
Trigeorgis, G., Bousmalis, K., Zafeiriou, S. & Schuller, B.. (2014). A Deep Semi-NMF Model for Learning Hidden Representations. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):1692-1700 Available from https://proceedings.mlr.press/v32/trigeorgis14.html.

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