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Poster
Multi-Label Prediction via Sparse Infinite CCA
Piyush Rai · Hal Daumé III

Wed Dec 09 07:00 PM -- 11:59 PM (PST) @ None #None

Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables. Building upon the recently suggested probabilistic interpretation of CCA, we propose a nonparametric, fully Bayesian framework that can automatically select the number of correlation components, and effectively capture the sparsity underlying the projections. In addition, given (partially) labeled data, our algorithm can also be used as a (semi)supervised dimensionality reduction technique, and can be applied to learn useful predictive features in the context of learning a set of related tasks. Experimental results demonstrate the efficacy of the proposed approach for both CCA as a stand-alone problem, and when applied to multi-label prediction.

Author Information

Piyush Rai (Duke University)
Hal Daumé III (Univ of Maryland / Microsoft Research)

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