Information criteria for reduced rank canonical correlation analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Canonical correlation analysis is an essential technique in the field of multivariate statistical analysis. In this paper, a framework involving unconstrained optimization criteria is proposed for extracting multiple canonical variates and canonical correlations serially and in parallel. These criteria are derived from optimizing three information based functions. Based on the gradient-ascent or descent methods, we derive many algorithms for performing the true CCA recursively. The main feature of this approach is that orthogonal basis for canonical variates is automatically obtained. The first few singular values and vectors can also be obtained using this framework. The performance of the proposed algorithms is demonstrated through simulations.

Original languageEnglish (US)
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages2215-2220
Number of pages6
DOIs
StatePublished - Dec 1 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume3
ISSN (Print)1098-7576

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period7/25/047/29/04

Keywords

  • Canonical correlation extraction
  • Fast adaptive algorithms
  • Information criteria

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