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Abstract:
This paper describes a clustering algorithm for vector
quantizers using a ``stochastic association model''. It offers a
new simple and powerful softmax adaptation rule. The adaptation
process is the same as the on-line K-means clustering method
except for adding random fluctuation in the distortion error
evaluation process. Simulation results demonstrate that the new
algorithm can achieve efficient adaptation as high as the
``neural gas'' algorithm, which is reported as one of the most
efficient clustering methods. It is a key to add uncorrelated
random fluctuation in the similarity evaluation process for each
reference vector. For hardware implementation of this process, we
propose a nanostructure, whose operation is described by a
single-electron circuit. It positively uses fluctuation in
quantum mechanical tunneling processes.
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