Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Trigeorgis:2017:10.1109/TPAMI.2016.2554555,
author = {Trigeorgis, G and Bousmalis, K and Zafeiriou, S and Schuller, B},
doi = {10.1109/TPAMI.2016.2554555},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {417--429},
title = {A deep matrix factorization method for learning attribute representations},
url = {http://dx.doi.org/10.1109/TPAMI.2016.2554555},
volume = {39},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 semisupervised 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.
AU  - Trigeorgis,G
AU  - Bousmalis,K
AU  - Zafeiriou,S
AU  - Schuller,B
DO  - 10.1109/TPAMI.2016.2554555
EP  - 429
PY  - 2017///
SN  - 0162-8828
SP  - 417
TI  - A deep matrix factorization method for learning attribute representations
T2  - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR  - http://dx.doi.org/10.1109/TPAMI.2016.2554555
UR  - http://hdl.handle.net/10044/1/32286
VL  - 39
ER  -
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