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