Joint Entropy Maximization in Kernel-Based Topographic Maps
نویسندگان
چکیده
منابع مشابه
Heteroscedastic Gaussian Kernel-Based Topographic Maps
Several learning algorithms for topographic map formation have been introduced that adopt overlapping activa-tion regions, rather than Voronoiregions, usually in the form of kernel functions. We review and introduce a numberof fixed point rules for training homogeneous, heteroscedastic but otherwise radially-symmetric Gaussian kernel-based topographic maps, or kernel topographic...
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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 shapes, and a new metric for clustering...
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When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during learning. The algorithm is applied to a ...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2002
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976602760128054