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<p>This correspondence proposes a fast Parzen density estimation algorithm that would be especially useful in 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 (1989) to further reduce the computational load. Experimental results are presented to verify the effectiveness of this algorithm.</p>
estimation theory; nonparametric statistics; pattern recognition; fast Parzen density estimation; clustering-based branch and bound; nonparametric discriminant analysis; data samples; kernel functions; multivariant case; data reduction technique; computational load

D. Landgrebe and B. Jeon, "Fast Parzen Density Estimation Using Clustering-Based Branch and Bound," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 950-954, 1994.
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