Functional analysis techniques to improve similarity matrices in discrimination problems Articles uri icon

publication date

  • September 2013

start page

  • 120

end page

  • 134

volume

  • 120

international standard serial number (ISSN)

  • 0047-259X

electronic international standard serial number (EISSN)

  • 1095-7243

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. (C) 2013 Elsevier Inc. All rights reserved.

keywords

  • classification; similarity measure; integral operator; mercer kernel; asymmetry; classifier function