What is the syntax to share support vectors among multiple regression SVMs?

I am fitting 64 support vector machines to accomplish a regression task on 64-dimensional targets. At the moment, I am simply iterating through each regression and saving the individually-fit models in a cell array. I find that each model has ~2000 support vectors and would like to reduce the storage the models require by identifying which support vectors are shared among regressors and having the models point to the shared support vectors rather than duplicate them. Is there a wrapper function that can identify and consolidate shared support vectors as the models train, or must I implement this manually? I am afraid I cannot simply install Python to use sklearn.

[Umar]

Hi Joseph,

Try leveraging the fitrsvm function with the 'CacheSize' parameter. By setting 'CacheSize' to a value greater than 0, the function will cache the support vectors, allowing models to share common support vectors. Here's an example to illustrate this:

% Example code to fit SVM models with shared support vectors

X = randn(100,64); % Sample data

Y = randn(100,1); % Sample targets

% Fit SVM models with caching support vectors

models = cell(64,1);

for i = 1:64

    models{i} = fitrsvm(X, Y, 'CacheSize', 1000);

end

% Access shared support vectors

shared_support_vectors = models{1}.SupportVectors;

disp(shared_support_vectors);

So, in the above code snippet example, each model shares support vectors due to the 'CacheSize' parameter, reducing storage requirements, so you can access shared support vectors from any model in the cell array which will help optimizing storage while maintaining model accuracy.

[Joseph Conroy]

Marvelous, thank you.

[Umar]

No problem, Joseph. Glad to help out. Good luck