NDLI: Measure transformed canonical correlation analysis with application to financial data

Content Provider	IEEE Xplore Digital Library
Author	Todros, K. Hero, A.O.
Copyright Year	2012
Description	Author affiliation: Dept. of Electrical Engineering and Computer Science, University of Michigan, Ann-Arbor 48105, U.S.A (Todros, K.; Hero, A.O.)
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.
Starting Page	361
Ending Page	364
File Size	426288
Page Count	4
File Format	PDF
ISBN	9781467310703
ISSN	2151870X
e-ISBN	9781467310710
DOI	10.1109/SAM.2012.6250511
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Joints Vectors Electrooculography Companies Universal Serial Bus Transforms probability measure transform Association analysis multivariate data analysis
Content Type	Text
Resource Type	Article

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