Kernel Density Construction Using Orthogonal Forward Regression

Abstract— The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model 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. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse...

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Sheng Chen, Xia Hong, Chris J. Harris
• Keywords: Density Construction, IDEAL 2004, Kernel Density Estimates, Sparse Kernel Density