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.

"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"])

Load a more illustrative data set

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

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

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

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.

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.

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