Relational Topographic Maps
نویسندگان
چکیده
We introduce relational variants of neural topographic maps including the selforganizing map and neural gas, which allow clustering and visualization of data given in terms of a pairwise similarity or dissimilarity matrix. It is assumed that this matrix originates from an euclidean distance or dot product, respectively, however, the underlying embedding of points is unknown. One can equivalently formulate batch optimization for topographic map formation in terms of the given similarities or dissimialities, respectively, thus providing a way to transfer batch optimization to relational data. For this procedure, convergence is guaranteed and extensions such as the integration of label information can readily be extended to this framework.
منابع مشابه
Topographic Mapping of Large Dissimilarity Data Sets
Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature encoding of information. Often ...
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