# Questions tagged [canonical-correlation]

Canonical correlation analysis (CCA) is a method to analyze correlations between two sets of variables. It finds linear combinations of variables in each set such that their correlation is maximal.

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### Canonical correlation analysis when one of the matrices consists of binary data

I have to do canonical correlation analysis between two multivariate datasets X and Y. One dataset contain numerical data and the other binary data. I would like to know what features are highly ...
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### Choosing the best featureset for prediction

I have this N sets of features F each with $F_i$ number of features. All the feature sets have 20000 examples and we have 20,000 labels for them. Lets say feature set $F_1$ has 10 features and ...
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### Canonical Correlation Definitions

Suppose that we have two sets of variables $\textbf{X}$ and $\textbf{Y}$. Let $$\textbf{X} = \begin{pmatrix} X_1 \\ X_2\\ \vdots \\X_p \end{pmatrix}$$ and  \textbf{Y} = \begin{pmatrix} Y_1 \\ Y_2\\ ...
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### Canonical Correlation analysis without raw data (algebra of CCA)

I want to run a Canonical Correlation (in R) but I don't have the original (raw) data. I have only the correlation matrix of all the variables. I have seen some ...
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### Statistical significance in canonical correlation analysis

I do canonical correlation analysis between two multivariate datasets $X$ and $Y$. For each pair of canonical variates (x-y pair) I get the canonical correlation coefficient. How can I test its ...
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### How to visualize what canonical correlation analysis does (in comparison to what principal component analysis does)?

Canonical correlation analysis (CCA) is a technique related to principal component analysis (PCA). While it is easy to teach PCA or linear regression using a scatter plot (see a few thousand examples ...
615 views

### Is CCA between two identical datasets equivalent to PCA on this dataset?

Reading Wikipedia about canonical correlation analysis (CCA) for two random vectors $X$ and $Y$, I was wondering if principal component anslysis (PCA) is the same as CCA when $X=Y$?
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### Canonical correlation analysis with rank correlation

Canonical correlation analysis (CCA) aims to maximize the usual Pearson product-moment correlation (i.e. linear correlation coefficient) of the linear combinations of the two data sets. Now, consider ...
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### Difference between canonical correpondence analysis and canonical correlation analysis

I am bit confused between two terms Canonical Correpondence Analysis and Canonical Correlation Analysis. Are the two some how related or they are entirely different techniques? Do they point to ...
529 views

### Books about model selection in ecology

I am an ecology student and have to deal with 10 or 20 field variables, including species frequencies. I need to screen out what variables are most important in the occurrence of a bird species. What ...
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### What is the relationship between regression and linear discriminant analysis (LDA)?

Is there a relationship between regression and linear discriminant analysis (LDA)? What are their similarities and differences? Does it make any difference if there are two classes or more than two ...
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### Eigenvalues nearly all 1 in canonical correlation analysis

I ran a Canonical Correlation Analysis on about 845 cases with 1000 variables each. (It originally started with 1000 cases and 400 variables but by using a kernel I got a 1000x1000 matrix) As a ...
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### PCA, LDA, CCA, and PLS

How are PCA, LDA, CCA, and PLS related? They all seem "spectral" and linear algebraic and very well understood (say 50+ years of theory built around them). They are used for very different things (PCA ...
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### CCA/KCCA for more than two views

Canonical Correlation Analysis (CCA) (and its kernel equivalent (KCCA)) can be used to find linear (nonlinear) relationships between two aligned multivariate datasets (or views). Is there a way to ...
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### Canonical correlation analysis with continuous and binary data

I came across interesting article on application of canonical correlation analysis (CCA). Authors apply classical CCA on a mixed variables dataset (both independent and dependent sets include ...
I'm writing code to approximate a function $y=f(\vec{x})$ where $y\in\mathbb{R}$ and $\vec{x}\in\mathbb{R}^N$ for medium-sized $N$ ($N$ between 20 and 50, approx.). I have a ton of examples, however, ...