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Abstract

We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time,
and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has
many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We
overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to
efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis
often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.