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Metric learning for incorporating privileged information in prototype-based models

Fouad, Shereen (2013)
Ph.D. thesis, University of Birmingham.

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Abstract

Prototype-based classification models, and particularly Learning Vector Quantization (LVQ) frameworks with adaptive metrics, are powerful supervised classification techniques with good generalization behaviour. This thesis proposes three advanced learning methodologies, in the context of LVQ, aiming at better classification performance under various classification settings. The first contribution presents a direct and novel methodology for incorporating valuable privileged knowledge in the LVQ training phase, but not in testing. This is done by manipulating the global metric in the input space, based on distance relations revealed by the privileged information. Several experiments have been conducted that serve as illustration, and demonstrate the benefit of incorporating privileged information on the classification accuracy. Subsequently, the thesis presents a relevant extension of LVQ models, with metric learning, to the case of ordinal classification problems. Unlike in existing nominal LVQ, in ordinal LVQ the class order information is explicitly utilized during training. Competitive results have been obtained on several benchmarks, which improve upon standard LVQ as well as benchmark ordinal classifiers. Finally, a novel ordinal-based metric learning methodology is presented that is principally intended to incorporate privileged information in ordinal classification tasks. The model has been verified experimentally through a number of benchmark and real-world data sets.

Type of Work:Ph.D. thesis.
Supervisor(s):Tino, Peter
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Subjects:QA76 Computer software
Institution:University of Birmingham
ID Code:4615
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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