Toward a compression-based self-organizing recognizer: Preliminary implementation of PRDC-CSOR
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. (C) 2013 Elsevier B.V. All rights reserved.
self organization kolmogorov complexity design scheme data analysis compressibility feature