Multi-task regularization of generative similarity models

Authors:
Luca Cazzanti;Sergey Feldman;Maya R. Gupta;Michael Gabbay
Affiliations:
Applied Physics Laboratory and University of Washington, Seattle;Dept. Electrical Engineering, University of Washington, Seattle;Dept. Electrical Engineering, University of Washington, Seattle;Applied Physics Laboratory and University of Washington, Seattle
Venue:
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Year:
2011

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Abstract

We investigate a multi-task approach to similarity discriminant analysis, where we propose treating the estimation of the different class-conditional distributions of the pairwise similarities as multiple tasks. We show that regularizing these estimates together using a leastsquares regularization weighted by a task-relatedness matrix can reduce the resulting maximum a posteriori classification errors. Results are given for benchmark data sets spanning a range of applications. In addition, we present a new application of similarity-based learning to analyzing the rhetoric of multiple insurgent groups in Iraq. We show how to produce the necessary task relatedness information from standard given training data, as well as how to derive task-relatedness information if given side information about the class relatedness.