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VARIABLE SELECTION ALONG CANONICAL VECTORS
Author(s) -
Campbell N.A.,
Furby S.L.
Publication year - 1994
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1994.tb00860.x
Subject(s) - selection (genetic algorithm) , context (archaeology) , random variate , canonical correlation , canonical analysis , canonical correspondence analysis , group (periodic table) , computer science , cover (algebra) , interpretation (philosophy) , matrix (chemical analysis) , canonical form , artificial intelligence , feature selection , mathematics , variable (mathematics) , algorithm , machine learning , statistics , pure mathematics , random variable , geography , engineering , ecology , physics , mathematical analysis , materials science , abundance (ecology) , archaeology , composite material , biology , programming language , mechanical engineering , quantum mechanics
Summary When describing and displaying inter‐group relationships using canonical variate analysis, the question often arises: which variables contribute most to the group separation along particular canonical vectors? This problem is discussed in the context of an example which uses remotely sensed data to discriminate between cover classes. The appropriate between‐groups matrix to be input into subset selection procedures is identified. When combined with subsequent simplification of the vectors, the approach leads to a simpler interpretation of the canonical variate display.