We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high-dimensional spaces. We show that there are several regularities in the relative performance of conventional kernel bandwidth estimators across different tasks and dimensionalities. In particular, we find that the Silverman rule-of-thumb and maximal-smoothing principle estimators consistently perform competitively on most tasks and dimensions for the datasets considered.
Reference:
Van der Walt, C.M and Barnard, E. 2014. Kernel bandwidth estimation for non-parametric density estimation: a comparative study. In: Proceedings of the Twenty-Fourth Annual Symposium of the Pattern Recognition Association of South Africa, Johannesburg, South Africa, 3 December 2013
Van der Walt, C., & Barnard, E. (2013). Kernel bandwidth estimation for non-parametric density estimation: a comparative study. PRASA 2013 Proceedings. http://hdl.handle.net/10204/7236
Van der Walt, CM, and E Barnard. "Kernel bandwidth estimation for non-parametric density estimation: a comparative study." (2013): http://hdl.handle.net/10204/7236
Van der Walt C, Barnard E, Kernel bandwidth estimation for non-parametric density estimation: a comparative study; PRASA 2013 Proceedings; 2013. http://hdl.handle.net/10204/7236 .
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