An Architecture to Efficiently Learn Co-Similarities from Multi-view Datasets
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 different 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 efficient parallelization of the computations. This approach allows to split large datasets into a set of smaller ones.
KeywordsMultiview Learning Similarity Learning Co-clustering
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