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

Gilles Bisson 1, * Clément Grimal 1 
* Corresponding author
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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Submitted on : Wednesday, October 10, 2012 - 6:22:06 PM
Last modification on : Wednesday, July 6, 2022 - 4:25:00 AM
Long-term archiving on: : Friday, December 16, 2016 - 11:10:56 PM


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  • HAL Id : hal-00740749, version 1


Gilles Bisson, Clément Grimal. An Architecture to Efficiently Learn Co-Similarities from Multi-View Datasets. International Conference on Neural Information Processing (ICONIP), Nov 2012, Doha, Qatar. pp.184-193. ⟨hal-00740749⟩



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