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A Probabilistic Least Squares Approach to Ordinal Regression
P.K. Srijith1, Shirish Shevade1, and S. Sundararajan2
1Computer Science and Automation, Indian Institute of Science, India
srijith@csa.iisc.ernet.in
shirish@csa.iisc.ernet.in
2Yahoo! Labs, Bangalore, India
zensid@yahoo.com
Abstract. This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.
Keywords: Gaussian processes, ordinal regression, probabilistic least squares, cross-validation
LNAI 7691, p. 683 ff.
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© Springer-Verlag Berlin Heidelberg 2012