Measure transformed canonical correlation analysis with application to financial data

Koby Todros, Alfred O. Hero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

In this paper, a new nonlinear generalization of linear canonical correlation analysis (LCCA) is derived. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the considered pair of random vectors after transformation of their joint probability distribution. The proposed transform is structured by a pair of nonnegative functions called the MT-functions. It preserves statistical independence and maps the joint probability distribution into a set of joint probability measures on the joint observation space. Specification of MT-functions in the exponential family, leads to MTCCA, which, in contrast to LCCA, is capable of detecting nonlinear dependencies. In the paper, MTCCA is illustrated for recovery of a nonlinear system with known structure, and for construction of networks that analyze long-term associations between companies traded in the NASDAQ and NYSE stock markets.

Original languageEnglish
Title of host publication2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Pages361-364
Number of pages4
DOIs
StatePublished - 12 Oct 2012
Externally publishedYes
Event2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 - Hoboken, NJ, United States
Duration: 17 Jun 201220 Jun 2012

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)2151-870X

Conference

Conference2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Country/TerritoryUnited States
CityHoboken, NJ
Period17/06/1220/06/12

Keywords

  • Association analysis
  • multivariate data analysis
  • probability measure transform

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