Probabilistic models for multi-view semi-supervised learning and coding

This thesis investigates the problem of classification from multiple noisy sensors or modalities. Examples include speech and gesture interfaces and multi-camera distributed sensor networks. Reliable recognition in such settings hinges upon the ability to learn accurate classification models in the face of limited supervision and to cope with the relatively large amount of potentially redundant information transmitted by each sensor or modality (i.e., view). We investigate and develop novel multi view learning algorithms capable of learning from semi-supervised noisy sensor data, for automatically adapting to new users and working conditions, and for performing distributed feature selection on bandwidth limited sensor networks. We propose probabilistic models built upon multi-view Gaussian Processes (GPs) for solving this class of problems, and demonstrate our approaches for solving audio-visual speech and gesture, and multi-view object classification problems. Multi-modal tasks are good candidates for multi-view learning, since each modality provides a potentially redundant view to the learning algorithm. On audio-visual speech unit classification, and user agreement recognition using spoken utterances and head gestures, we demonstrate that multi-modal co-training can be used to learn from only a few labeled examples in one or both of the audio-visual modalities. We also propose a co-adaptation algorithm, which adapts existing audio-visual classifiers to a particular user or noise condition by leveraging the redundancy in the unlabeled data. Existing methods typically assume constant per-channel noise models.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Christoudias, C. Mario (Christos Mario)

Advisor dc:contributor.advisor

• Trevor J. Darrell.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/55096

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/55096