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3 edition of Introduction to uses and interpretation of principal component analysis in forest biology found in the catalog.

Introduction to uses and interpretation of principal component analysis in forest biology

J. G. Isebrands

Introduction to uses and interpretation of principal component analysis in forest biology

by J. G. Isebrands

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Published by North Central Forest Experiment Station, Forest Service, U.S. Dept. of Agriculture in [Saint Paul, Minn.] .
Written in English

    Subjects:
  • Principal components analysis,
  • Forests and forestry

  • Edition Notes

    Statementby J.G. Isebrands and Thomas R. Crow.
    SeriesGeneral technical report NC -- 17.
    ContributionsCrow, Thomas R.
    The Physical Object
    Pagination19 p. ;
    Number of Pages19
    ID Numbers
    Open LibraryOL17614369M
    OCLC/WorldCa2592985

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      1 Background. Principal component analysis (PCA) is a year old mathematical technique credited to Karl Pearson [] and its properties as well as the interpretation of components have been investigated extensively [].The technique has found application in many diverse fields such as ecology, economics, psychology, meteorology, oceanography, and by: A Statistical Manual For Forestry Research May FORESTRY RESEARCH SUPPORT PROGRAMME biology, forest mensuration and ecology many of which are unique to forestry research, are described. Throughout this book, names of variables are referred in italics.


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Introduction to uses and interpretation of principal component analysis in forest biology by J. G. Isebrands Download PDF EPUB FB2

Introduction to uses and interpretation of principal component analysis in forest biology. [Saint Paul, Minn.]: North Central Forest Experiment Station, Forest Service, U.S. Dept. of Agriculture, (OCoLC) Material Type: Government publication, National government publication: Document Type: Book: All Authors / Contributors.

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Are you an author. Learn about Author Central Cited by: Factor analysis is similar to principal component analysis, in that factor analysis also involves linear combinations of variables. Different from PCA, factor analysis is a correlation-focused approach seeking to reproduce the inter-correlations among variables, in which the factors "represent the common variance of variables, excluding unique.

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Written by. The main methods of feature extraction include principal component analysis [3] [4], linear discriminant analysis [5][6] [7], genetic algorithms [8], and factor analysis. Although feature.

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Principal component analysis is a statistical technique that is used to analyze the interrelationships among a large number of variables and to explain these variables in terms of a smaller number of variables, called principal components, with a minimum loss of information.

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