Results 211 to 220 of about 183,993 (251)

Generalised Canonical Correlation Analysis

2000
Canonical Correlation Analysis [3] is used when we have two data sets which we believe have some underlying correlation. In this paper, we derive a new family of neural methods for finding the canonical correlation directions by solving a generalized eigenvalue problem. Based on the differential equation for the generalized eigenvalue problem, a family
Zhenkun Gou, Colin Fyfe
openaire   +1 more source

KERNEL AND NONLINEAR CANONICAL CORRELATION ANALYSIS

International Journal of Neural Systems, 2000
We review a neural implementation of the statistical technique of Canonical Correlation Analysis (CCA) and extend it to nonlinear CCA. We then derive the method of kernel-based CCA and compare these two methods on real and artificial data sets before using both on the Blind Separation of Sources.
Pei Ling Lai, Colin Fyfe
openaire   +3 more sources

Canonical Correlation Analysis

2012
Canonical correlation analysis (CCA) is one of the most important tools of multivariate statistical analysis. Its extension to the functional context is not trivial, and in many ways illustrates the differences between multivariate and functional data. One of the most influential contributions has been made by Leurgans et al.
Lajos Horváth, Piotr Kokoszka
openaire   +2 more sources

Influence in Canonical Correlation Analysis

Psychometrika, 1992
The perturbation theory of the generalized eigenproblem is used to derive influence functions of each squared canonical correlation coefficient and the corresponding canonical vector pair. Three sample versions of these functions are described and some properties are noted.
openaire   +2 more sources

Canonical Correlation Analysis

2009
In canonical correlation analysis, the objective is to relate a set of dependent or criterion variables to another set of independent or predictor variables. In order to do that, we find a scalar, defined as a linear combination of the dependent variables, as well as a scalar defined as a linear combination of the independent variables.
openaire   +2 more sources

Canonical Correlation Analysis

2017
This chapter covers classical and robust canonical correlation analysis (CCA). Let \(\varvec{x}\) be the \(p \times 1\) vector of predictors, and partition \({\varvec{x}} = ({\varvec{w}}^T, {\varvec{y}}^T)^T\) where \({\varvec{w}}\) is \(m \times 1\) and \({\varvec{y}}\) is \(q \times 1\) with \(m = p-q \le q\) and \(m, q \ge 1\). If \(m = 1\) and \(q =
openaire   +2 more sources

Canonical Correlation Analysis

2019
In many applications, one wants to associate one kind of data with another. For example, every data item could be a video sequence together with its sound track. You might want to use this data to learn to associate sounds with video, so you can predict a sound for a new, silent, video.
openaire   +1 more source

A Tutorial on Canonical Correlation Methods

ACM Computing Surveys, 2018
Delmiro Fernández-Reyes, Juho Rousu, John Shawe-Taylor   +2 more
exaly  

On the permutation test in canonical correlation analysis

Computational Statistics and Data Analysis, 2006
Takakazu Sugiyama
exaly  

Fault detection of process correlation structure using canonical variate analysis-based correlation features

Journal of Process Control, 2017
Benben Jiang, Richard D Braatz
exaly