On the Correlation Measurement of Data Representations
The correlation measurement between data representations is the basis of machine learning and artificial intelligence techniques.However,existing methods either overlook the global information on the involved representations partially or scale poorly.To address the issue,this paper proposes the Representation Alignment(RA)to globally measure the correlation between two arbitrary data representations in linear time to the dimension and the number of samples.We further extend the representation alignment to the Contrastive Representation Alignment(CRA),where the concerned representations are two augmentations(in the scope of contrastive learning)of data samples.In addition,the two alignments can be naturally applied in multi-view learning systems by maximizing them on the latent representations of different data views so as to integrate their complementary information.To validate this,we develop two novel multi-view clustering algorithms and achieve state-of-the-art performance on seven benchmark datasets.
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