Variants of unsupervised kernel regression: General cost functions

Klanke S, Ritter H (2007)
Neurocomputing 70(7-9): 1289-1303.

Journal Article | Published | English

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Abstract
We present an extension to unsupervised kernel regression (UKR), a recent method for learning of nonlinear manifolds, which can utilize leave-one-out cross-validation as an automatic complexity control without additional computational cost. Our extension allows us to incorporate general cost functions, by which the UKR algorithm can be made more robust or be tuned to specific noise models. We focus on Huber's loss and on the E-insensitive loss, which we present together with a practical optimization approach. We demonstrate our method on both toy and real data. (c) 2007 Elsevier B.V. All rights reserved.
Publishing Year
Conference
14th Annual European Symposium on Artificial Neural Networks
Location
Bruges, Belgium
Conference Date
2006-04-26 – 2006-04-28
ISSN
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Cite this

Klanke S, Ritter H. Variants of unsupervised kernel regression: General cost functions. Neurocomputing. 2007;70(7-9):1289-1303.
Klanke, S., & Ritter, H. (2007). Variants of unsupervised kernel regression: General cost functions. Neurocomputing, 70(7-9), 1289-1303.
Klanke, S., and Ritter, H. (2007). Variants of unsupervised kernel regression: General cost functions. Neurocomputing 70, 1289-1303.
Klanke, S., & Ritter, H., 2007. Variants of unsupervised kernel regression: General cost functions. Neurocomputing, 70(7-9), p 1289-1303.
S. Klanke and H. Ritter, “Variants of unsupervised kernel regression: General cost functions”, Neurocomputing, vol. 70, 2007, pp. 1289-1303.
Klanke, S., Ritter, H.: Variants of unsupervised kernel regression: General cost functions. Neurocomputing. 70, 1289-1303 (2007).
Klanke, Stefan, and Ritter, Helge. “Variants of unsupervised kernel regression: General cost functions”. Neurocomputing 70.7-9 (2007): 1289-1303.
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