Fast Parzen Density Estimation Using Clustering-Based Branch and Bound

This correspondence proposes a fast Parzen density estimation algorithm that would be especially useful in the nonparametric discriminant analysis problems. By preclustering the data and applying a simple branch and bound procedure to the clusters, significant numbers of data samples that would contribute little to the density estimate can be excluded without detriment to actual evaluation via the kernel functions. This technique is especially helpful in the multivariant case, and does not require a uniform sampling grid. The proposed algorithm may also be used in conjunction with the data reduction technique of Fukunaga and Hayes to further reduce the computational load. Experimental results are presented to verify the effectiveness of this algorithm.