# scanoncorr

Version 1.0.0 (85.4 KB) by
Sparse canonical correlation analysis
87 Downloads
Updated 4 Jul 2023

# Sparse canonical correlation analysis

scanoncorr performs sparse canonical correlation analysis in MATLAB. The algorithm is based on the alternating projected gradient approach presented in [1]. Sparsity is induced using L1-norm constraints on the canonical coefficient vectors.

## Quick start

"Installing" a MATLAB package is easy: just clone this repository and add it to your MATLAB path:

addpath('path_to/scanoncorr')


Set up some data

load carbig;
data = [Displacement Horsepower Weight Acceleration MPG];
nans = sum(isnan(data),2) > 0;
X = data(~nans,1:3); Y = data(~nans,4:5);


Choose sparsity parameters and calculate canonical coefficients

cx = 0.1;
cy = 0.1;
[A B r U V] = scanoncorr(X,Y,cx,cy);


Visualise the results

figure
subplot(2,2,1:2)
gscatter(U,V,Cylinders(~nans))
subplot(2,2,3)
bar(A)
xticklabels(["Displacement","Horsepower","Weight"])
subplot(2,2,4)
bar(B)
xticklabels(["Acceleration","MPG"])


## More detailed instructions

Load a more illustrative data set

load scanoncorr_example
X = data.X; Y = data.Y;


### Multiple canonical vectors

You can find multiple canonical vectors using the option 'D'

[A B] = scanoncorr(X,Y,cx,cy,'D',2);


### Choosing the initialisation method

A and B have to be seeded at the start of the algorithm. By default this is done using singular vectors of the cross-covariance matrix. Another option is to try several random starts and pick the result that achieves the best value for the objective

[A B] = scanoncorr(X,Y,cx,cy,'init','random');


The two options can also be combined. Here we try first the singular vectors and then 10 random starts

[A B] = scanoncorr(X,Y,cx,cy,'rStarts',10);


Note that the default 'svd' option using singular vectors usually performs the best.

### Optimising the hyperparameters

The function optimiseScanoncorrParameters can be used to find the best values for the regularisation parameters cx and cy, and to pick the initialisation method. It performs cross-validation over a grid of cx and cy values and picks the combination of parameters that performs the best on average.

[optInit,optCx,optCy,results] = optimiseScanoncorrParameters(X,Y)


This function produces two figures, one for each initialisation method, displaying the average correlation in the test set, as well as the approximate cardinality of A and B.

## References

[1] Uurtio, Viivi, Sahely Bhadra, and Juho Rousu. "Large-scale sparse kernel canonical correlation analysis." International Conference on Machine Learning. PMLR, 2019.

### Cite As

Taneli Pusa (2024). scanoncorr (https://github.com/htpusa/scanoncorr), GitHub. Retrieved .

##### MATLAB Release Compatibility
Created with R2022b
Compatible with any release
##### Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.