Robust Multi-View Representation Learning (Student Abstract)

Multi-view data has become ubiquitous, especially with multi-sensor systems like self-driving cars or medical patientside monitors. We propose two methods to approach robust multi-view representation learning with the aim of leveraging local relationships between views. The first is an extension of Canonical Correlation Analysis (CCA) where we consider multiple one-vs-rest CCA problems, one for each view. We use a group-sparsity penalty to encourage finding local relationships. The second method is a straightforward extension of a multi-view AutoEncoder with view-level drop-out. We demonstrate the effectiveness of these methods in simple synthetic experiments. We also describe heuristics and extensions to improve and/or expand on these methods.

Authors

• Sibi Venkatesan
• James K. Miller
• Artur Dubrawski Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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