Number of the records: 1
Correlation Dimension-Based Classifier
- 1.
SYSNO ASEP 0421968 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Correlation Dimension-Based Classifier Author(s) Jiřina, Marcel (UIVT-O) SAI, RID
Jiřina jr., M. (CZ)Source Title IEEE Transactions on Cybernetics - ISSN 2168-2267
Roč. 44, č. 12 (2014), s. 2253-2263Number of pages 11 s. Language eng - English Country US - United States Keywords classifier ; multidimensional data ; correlation dimension ; scaling exponent ; polynomial expansion Subject RIV BB - Applied Statistics, Operational Research R&D Projects LG12020 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UIVT-O - RVO:67985807 UT WOS 000345629000002 EID SCOPUS 84911928407 DOI 10.1109/TCYB.2014.2305697 Annotation Correlation dimension, singularity exponents, also scaling exponents are widely used in multifractal chaotic series analysis. Correlation dimension and other measures of effective dimensionality are used for characterization of data in applications. A direct use of correlation dimension to multidimensional data classification has not been hitherto presented. There are observations that the correlation integral is a distribution function of distances between all pairs of data points, and that by using polynomial expansion of distance with exponent equal to the correlation dimension this distribution is transformed into locally uniform. The classifier is based on consideration that the "influence" of neighbor points of some class on the probability that the query point belongs to this class is inversely proportional to its distance to the correlation dimension - power. New classification approach is based on summing up all these influences for each class. We prove that a resulting formula gives an estimate of probability of class - not a measure of membership to a class only - to which the query point belongs. For this assertion to be valid it is necessary that exponent of the polynomial transformation must be the correlation dimension. We also propose an "averaging approach" that speeds up computation of the correlation dimension especially for large data sets. It is demonstrated that the correlation dimension based classifier can outperform more sophisticated classifiers. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2015
Number of the records: 1