منابع مشابه
Principal Components Analysis , Exploratory Factor Analysis , and Confirmatory Factor Analysis
Principal components analysis and factor analysis are common methods used to analyze groups of variables for the purpose of reducing them into subsets represented by latent constructs (Bartholomew, 1984; Grimm & Yarnold, 1995). Even though PCA shares some important characteristics with factor analytic methods such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), the ...
متن کاملPersian Handwriting Analysis Using Functional Principal Components
Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...
متن کاملThe Truth about Principal Components and Factor Analysis
Principal components tries to re-express the data as a sum of uncorrelated components. There are lots of other techniques which try to do similar things, like Fourier analysis, or wavelet decomposition. Things like Fourier analysis decompose the data into a sum of a fixed set of basis functions or basis vectors. This has the advantage of making results comparable across data sets, and of making...
متن کاملThe Truth about Principal Components and Factor Analysis
2 The Truth about Factor Analysis 6 2.1 How Many Factors? . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 The Graphical Form of Factor Models . . . . . . . . . . . . . . . 8 2.3 The Rotation Problem Again . . . . . . . . . . . . . . . . . . . . 10 2.4 Factors or Mixtures? . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.5 The Thomson Sampling Model . . . . . . . . . . . . . . . . ...
متن کاملOnline Principal Components Analysis
We consider the online version of the well known Principal Component Analysis (PCA) problem. In standard PCA, the input to the problem is a set of ddimensional vectors X = [x1, . . . ,xn] and a target dimension k < d; the output is a set of k-dimensional vectors Y = [y1, . . . ,yn] that minimize the reconstruction error: minΦ ∑ i ‖xi − Φyi‖2. Here, Φ ∈ Rd×k is restricted to being isometric. The...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 1995
ISSN: 0047-259X
DOI: 10.1006/jmva.1995.1069