Optimal Smoothing of Kernel-Based Topographic Maps with Application to Density-Based Clustering of Shapes


A crucial issue when applying topographic maps for clustering purposes is how to select the map’s overall degree of smoothness. In this paper, we develop a new strategy for optimally smoothing, by a common scale factor, the density estimates generated by Gaussian kernel-based topographic maps. We also introduce a new representation structure for images of… (More)
DOI: 10.1023/B:VLSI.0000027486.56120.e7


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