In principal component analysis and related techniques, we approximate (in the least squares sense) as n x m matrix F by an n x m matrix G which satisfies rank(G) ≤ p, where p < min(n,m). Or equivalenty, we want to find an n x p matrix X and an m x p matrix Y such that G = XY' approximates F as closely as possible. The rows of X and Y are then often used in graphical displays. In particular, biplots represent X and Y jointly as n + m points in Euclidean p space
Abstract: In order to interpret the biplot it is necessary to know which points – usually variables...
The fundamental geometry is outlined that underlies all biplots of a data-matrix X of n cases and p...
In the practice of information extraction, the input data are usually arranged into pattern matrices...
In principal component analysis and related techniques, we approximate (in the least squares sense) ...
We construct a weighted Euclidean distance that approximates any distance or dissimilarity measure b...
Abstract: We construct a weighted Euclidean distance that approximates any distance or dissimilarit...
We construct a weighted Euclidean distance that approximates any distance or dissimilarity measure b...
This paper establishes a general framework for metric scaling of any distance measure between indivi...
Biplots are the multivariate analog of scatter plots, approximating the multivariate distribution of...
Biplots are a graphical method for simultaneously displaying two kinds of information; typically, th...
Attention is drawn to some useful but not generally known properties of principal components analysi...
Principal component analysis is a versatile statistical method for reducing a cases-by-variables da...
We study the problem of computing the similarity between two piecewise-linear bivariate functions de...
We study the problem of computing correlation between two piecewise-linear bivariate functions defin...
Biplots display interunit distances, as well as variances and correlations of variables of large dat...
Abstract: In order to interpret the biplot it is necessary to know which points – usually variables...
The fundamental geometry is outlined that underlies all biplots of a data-matrix X of n cases and p...
In the practice of information extraction, the input data are usually arranged into pattern matrices...
In principal component analysis and related techniques, we approximate (in the least squares sense) ...
We construct a weighted Euclidean distance that approximates any distance or dissimilarity measure b...
Abstract: We construct a weighted Euclidean distance that approximates any distance or dissimilarit...
We construct a weighted Euclidean distance that approximates any distance or dissimilarity measure b...
This paper establishes a general framework for metric scaling of any distance measure between indivi...
Biplots are the multivariate analog of scatter plots, approximating the multivariate distribution of...
Biplots are a graphical method for simultaneously displaying two kinds of information; typically, th...
Attention is drawn to some useful but not generally known properties of principal components analysi...
Principal component analysis is a versatile statistical method for reducing a cases-by-variables da...
We study the problem of computing the similarity between two piecewise-linear bivariate functions de...
We study the problem of computing correlation between two piecewise-linear bivariate functions defin...
Biplots display interunit distances, as well as variances and correlations of variables of large dat...
Abstract: In order to interpret the biplot it is necessary to know which points – usually variables...
The fundamental geometry is outlined that underlies all biplots of a data-matrix X of n cases and p...
In the practice of information extraction, the input data are usually arranged into pattern matrices...