Results 291 to 300 of about 2,294,043 (337)
Some of the next articles are maybe not open access.
Principal Components and Principal Clusters
Journal of Information and Optimization Sciences, 1987Abstract The use of principal components to reduce the number of dimensions so that graphieal representation of the data is possible has been developed. One. of the most important applications is the connexion with cluster analysis. It has not been defined the criteria by which to decide whether there is any justification for dividing a set of ...
Haruo Miyazaki, Youichi Seki
openaire +1 more source
2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Detlef, Groth +3 more
openaire +2 more sources
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Detlef, Groth +3 more
openaire +2 more sources
1986
The theory and practice of principal components are considered both from the point of view of statistical theory and from that of descriptive statistics. Some well known applications are briefly discussed.
Kloek, T., Kloek, T.
openaire +1 more source
The theory and practice of principal components are considered both from the point of view of statistical theory and from that of descriptive statistics. Some well known applications are briefly discussed.
Kloek, T., Kloek, T.
openaire +1 more source
Which principal components to utilize for principal component regression
Journal of Chemometrics, 1992AbstractPrincipal components (PCs) for principal component regression (PCR) have historically been selected from the top down for a reliable predictive model. That is, the PCs are arranged in a list starting with the most informative (PC associated with the largest singular value) and proceeding to the least informative (PC associated with the smallest
Jon M. Sutter +2 more
openaire +1 more source
Segmented principal component transform–principal component analysis
Chemometrics and Intelligent Laboratory Systems, 2005Abstract A new approach to perform Principal Component Analysis (PCA) on very wide matrices is proposed in this work. The procedure is based on an extension of the Principal Component Transform (PCT) concept—the PCT being applied to non-superimposed segments of the data matrix.
António S. Barros, Douglas N. Rutledge
openaire +1 more source
The efficient cross-validation of principal components applied to principal component regression
Statistics and Computing, 1995The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components.
B. J. A. Mertens, T. Fearn, M. Thompson
openaire +1 more source
Principal components or principal axes
1999The expression principal components first appeared in the writings of the American statistician Harold Hotelling in 1933, but the technique was known earlier as principal axes and goes back to Karl Pearson (1901). In principle, principal components are appropriate for the analysis of variation within a sample coming from a single statistical population
openaire +1 more source
The principal components of meaning, revisited
Psychonomic Bulletin & ReviewOsgood, Suci, and Tannebaum were the first to attempt to identify the principal components of semantics using dimensional reduction of a high-dimensional model of semantics constructed from human judgments of word relatedness. Modern word-embedding models analyze patterns of words to construct higher dimensional models of semantics that can be ...
Chris, Westbury +2 more
openaire +2 more sources
2012
Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observed k-dimensional variables in such a way that the new variables are vectors of k mutually orthogonal (uncorrelated) components – the principal components – ranked ...
Hallin, Marc, Hörmann, Siegfried
openaire +1 more source
Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observed k-dimensional variables in such a way that the new variables are vectors of k mutually orthogonal (uncorrelated) components – the principal components – ranked ...
Hallin, Marc, Hörmann, Siegfried
openaire +1 more source