Functional analysis techniques to improve similarity matrices in discrimination problems

Javier González and Alberto Muñoz

Journal of Multivariate Analysis, 2013, vol. 120, issue C, 120-134

Abstract: In classification problems an appropriate choice of the data similarity measure is a key step to guarantee the success of discrimination procedures. In this work, we propose a general methodology to transform the available data similarity S, incorporating the data labels, to improve the performance of discrimination procedures. We will focus on the case when S is asymmetric. We study the precise connection between similarity matrices and integral operators that will allow the evaluation of the transformed matrix on test points. The proposed methodology is used in several simulated and real experiments where the performance of several discrimination techniques is improved.

Keywords: Classification; Similarity measure; Integral operator; Mercer kernel; Asymmetry; Classifier function (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13000742
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:120:y:2013:i:c:p:120-134

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.04.013

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().