An Architecture to Efficiently Learn Co-Similarities from Multi-View Datasets

Gilles Bisson 1, * Clément Grimal 1
* Auteur correspondant
Abstract : In this paper, we introduce the MVSim architecture which is able to cluster multi-view datasets (i.e. datasets containing several objects linked together by di erent relations), by using several instances of a co-similarity algorithm.We show that this framework provides better results than existing approaches, while reducing both time and space complexities thanks to an e cient parallelization of the computations. This approach allows to split large datasets into a set of smaller ones.
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Communication dans un congrès
T. Huang, Z. Zeng, C. Li, C.S. Leung. International Conference on Neural Information Processing (ICONIP), Nov 2012, Doha, Qatar. Springer, 7663, pp.184-193, 2012, Lecture Notes in Computer Science (LNCS)
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Dernière modification le : mardi 28 octobre 2014 - 18:34:55
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Gilles Bisson, Clément Grimal. An Architecture to Efficiently Learn Co-Similarities from Multi-View Datasets. T. Huang, Z. Zeng, C. Li, C.S. Leung. International Conference on Neural Information Processing (ICONIP), Nov 2012, Doha, Qatar. Springer, 7663, pp.184-193, 2012, Lecture Notes in Computer Science (LNCS). <hal-00740749>

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