Validation based sparse Gaussian processes for ordinal regression
Data(s) |
2012
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Resumo |
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. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/47814/1/lncs_7664-409_2012.pdf 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. |
Publicador |
Springer |
Relação |
http://dx.doi.org/10.1007/978-3-642-34481-7_50 http://eprints.iisc.ernet.in/47814/ |
Palavras-Chave | #Computer Science & Automation (Formerly, School of Automation) |
Tipo |
Conference Paper PeerReviewed |