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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 (University of Maryland - College Park)
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2021 : Poster: The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
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2021 : The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
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2018 Workshop: Wordplay: Reinforcement and Language Learning in Text-based Games »
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2014 Workshop: Representation and Learning Methods for Complex Outputs »
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2012 Poster: Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression »
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2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
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2011 Poster: Message-Passing for Approximate MAP Inference with Latent Variables »
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2010 Poster: Learning Multiple Tasks using Manifold Regularization »
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2010 Poster: Co-regularization Based Semi-supervised Domain Adaptation »
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2008 Poster: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression »
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2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
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