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.
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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 |
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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 |
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