Competitive Winner-Takes-All Clustering in the Domain of Graphs
نویسنده
چکیده
We present a theoretical foundation for competitive learning in the domain of graphs within a connectionist framework. In the first part of this contribution we embed graphs in an Euclidean space to facilitate competitive learning in the domain of graphs. We adopt constitutive concepts of competitive learning like the scalar products, metrics, and the weighted mean for graphs. The first part is independent of the particular graph matching algorithm for determining the best matching model in an Euclidean sense. The second part proposes a neural network model for competitive learning in the domain of graphs. The network consists of several recurrent subnets, each comparing the current input graph with a model graph. The subnets indirectly compete among each other via an inhibitory winner-takes-all network. To determine the best matching model the system follows the principle elimination of competition. It disables unfavorable subnets at an early stage of their evolution and focuses on promising subnets until the subnet corresponding to the best matching model wins the competition and all other subnets are disabled.
منابع مشابه
A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of Structures
We propose a winner-takes-all (WTA) classifier for structures represented by graphs. WTA classification follows the principle elimination of competition. The input structure is assigned to the class corresponding to the winner of the competition. In experiments we investigate the performance of the WTA classifier and compare it with the canonical maximum similarity (MS) classifier.
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