Semi-Supervised, Dimensionality Reduction via Canonical Correlation Analysis

Abstract

We analyze the multi-view regression problemwhere we have two views (X1, X2) of the input data and a real target variable Y of interest. In a semi-supervised learning setting, we consider two separate assumptions (one based on redundancy and the other based on (de)correlation) and show how, under either assumption alone, dimensionality reduction (based on… (More)

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