Learning discriminative models with incomplete data

Many practical problems in pattern recognition require making inferences using multiple modalities, e.g. sensor data from video, audio, physiological changes etc. Often in real-world scenarios there can be incompleteness in the training data. There can be missing channels due to sensor failures in multi-sensory data and many data points in the training set might be unlabeled. Further, instead of having exact labels we might have easy to obtain coarse labels that correlate with the task. Also, there can be labeling errors, for example human annotation can lead to incorrect labels in the training data. The discriminative paradigm of classification aims to model the classification boundary directly by conditioning on the data points; however, discriminative models cannot easily handle incompleteness since the distribution of the observations is never explicitly modeled. We present a unified Bayesian framework that extends the discriminative paradigm to handle four different kinds of incompleteness. First, a solution based on a mixture of Gaussian processes is proposed for achieving sensor fusion under the problematic conditions of missing channels. Second, the framework addresses incompleteness resulting from partially labeled data using input dependent regularization.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Kapoor, Ashish, 1977-

Advisor dc:contributor.advisor

• Rosalind W. Picard.

• Architecture. Program In Media Arts and Sciences

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/1721.1/34181
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/34181

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34181