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Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Ning Chen · Jun Zhu · Eric Xing

Tue Dec 07 12:00 AM -- 12:00 AM (PST) @

Learning from multi-view data is important in many applications, such as image classification and annotation. In this paper, we present a large-margin learning framework to discover a predictive latent subspace representation shared by multiple views. Our approach is based on an undirected latent space Markov network that fulfills a weak conditional independence assumption that multi-view observations and response variables are independent given a set of latent variables. We provide efficient inference and parameter estimation methods for the latent subspace model. Finally, we demonstrate the advantages of large-margin learning on real video and web image data for discovering predictive latent representations and improving the performance on image classification, annotation and retrieval.

Author Information

Ning Chen (Tsinghua University)
Jun Zhu (Tsinghua University)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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