Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Takashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata
This paper describes a clustering algorithm for vector quantizers using a “stochastic association model”. It offers a new simple and powerful soft- max 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 simi- larity evaluation process for each reference vector. For hardware imple- mentation 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.