Toward a compression-based self-organizing recognizer: Preliminary implementation of PRDC-CSOR |
| |
Authors: | Toshinori Watanabe |
| |
Affiliation: | Graduate School of Information Systems, University of Electro-Communications, 1-5-1 Chofu-ga-oka, Chofu-shi, Tokyo 182-8585, Japan |
| |
Abstract: | The present paper introduces a new data analyzer, a compression-based self-organizing recognizer, the PRDC-CSOR (Pattern Representation scheme using Data Compression – Compression based Self ORganizing Recognizer), with a preliminary application to image data. The PRDC-CSOR is an extension of the authors’ previously proposed pattern representation scheme using data compression (PRDC). Contrary to the traditional statistical-model-based recognition system methods, the PRDC-CSOR constructs itself using incoming data only. The basic tool, compressibility, is an approximation of the Kolmogorov complexity K(x) defined in an individual text x as a countermeasure against the Shannon entropy H(X) defined on an ensemble X. Due to this feature, a highly automatic self-organizing recognition system becomes possible as demonstrated in this paper. |
| |
Keywords: | Design scheme Self organization Data analysis Compressibility feature Kolmogorov complexity |
本文献已被 ScienceDirect 等数据库收录! |
|