A Sparse Kernel Density Estimation Algorithm Using Forward Constrained Regression

Abstract. Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate. Key words: cross validation, jackknife parameter estimator, Parzen window, probability density function, sparse modelling.

• Type: Conference
• Year: 2007
• Where: ICIC
• Authors: Xia Hong, Sheng Chen, Chris Harris
• Keywords: ICIC 2007, Kernel Density Estimators, Parzen Window, Sparse Kernel Density