An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Chen, S., Hong, X., & Harris, C. J. (2004). Kernel density construction using orthogonal forward regression. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 586–592. https://doi.org/10.1007/978-3-540-28651-6_86
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