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Poster
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) @
in Poster Session »

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.

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Author Information

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

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