Correlation Dimension-Based Classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00421968" target="_blank" >RIV/67985807:_____/14:00421968 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/14:00226476
Result on the web
<a href="http://dx.doi.org/10.1109/TCYB.2014.2305697" target="_blank" >http://dx.doi.org/10.1109/TCYB.2014.2305697</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCYB.2014.2305697" target="_blank" >10.1109/TCYB.2014.2305697</a>
Alternative languages
Result language
angličtina
Original language name
Correlation Dimension-Based Classifier
Original language description
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 thatthe 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 formul
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/LG12020" target="_blank" >LG12020: Advanced statistical analysis and non-statistical separation techniques for physical processing detection in data sets sampled by means of elementary particle accelerators.</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Cybernetics
ISSN
2168-2267
e-ISSN
—
Volume of the periodical
44
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
2253-2263
UT code for WoS article
000345629000002
EID of the result in the Scopus database
—