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Learning discriminative models with incomplete data

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dc.contributor.advisor Rosalind W. Picard. en_US
dc.contributor.author Kapoor, Ashish, 1977- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US
dc.date.accessioned 2007-10-22T16:19:53Z
dc.date.available 2007-10-22T16:19:53Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://dspace.mit.edu/handle/1721.1/34181 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/34181
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006. en_US
dc.description Includes bibliographical references (p. 115-121). en_US
dc.description.abstract 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. en_US
dc.description.abstract (cont.) Third, we introduce the located hidden random field (LHRF) that learns finer level labels when only some easy to obtain coarse information is available. Finally the proposed framework can handle incorrect labels, the fourth case of incompleteness. One of the advantages of the framework is that we can use different models for different kinds of label errors, providing a way to encode prior knowledge about the process. The proposed extensions are built on top of Gaussian process classification and result in a modular framework where each component is capable of handling different kinds of incompleteness. These modules can be combined in many different ways, resulting in many different algorithms within one unified framework. We demonstrate the effectiveness of the framework on a variety of problems such as multi-sensor affect recognition, image classification and object detection and segmentation. en_US
dc.description.statementofresponsibility by Ashish Kapoor. en_US
dc.format.extent 121 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/34181 en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Architecture. Program In Media Arts and Sciences en_US
dc.title Learning discriminative models with incomplete data en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US
dc.identifier.oclc 69420289 en_US


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