Validation based sparse Gaussian processes for ordinal regression

Srijith, PK and Shevade, Shirish and Sundararajan, S (2012) Validation based sparse Gaussian processes for ordinal regression. In: ICONIP 2012 19th International Conference, November 12-15, 2012, Doha, Qatar.

PDF lncs_7664-409_2012.pdf - Published Version Restricted to Registered users only Download (209kB) | Request a copy

Abstract

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Item Type:	Conference Paper
Publisher:	Springer
Additional Information:	Copyright of this article belongs to Springer.
Keywords:	Gaussian Processes; Ordinal Regression; Sparse Models
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	22 Nov 2013 11:28
Last Modified:	22 Nov 2013 11:31
URI:	http://eprints.iisc.ac.in/id/eprint/47814

Actions (login required)

View Item