Hi Joseph,
To address the issue of shared support vectors across multiple Support Vector Machines (SVMs) in a regression task without duplicating them, you'll likely need to implement a custom solution, especially if you're constrained to a specific programming environment and cannot use Python's scikit-learn.
Here's a high-level approach to manually consolidate shared support vectors:
- After training each SVM, extract the support vectors and their indices. You can then compare these support vectors across different models to identify duplicates.
- Use a data structure (e.g., a dictionary or map) to store unique support vectors. Each unique support vector is stored once, and each model stores a reference (e.g., an index or pointer) to the shared support vector.
- Instead of storing the full support vectors in each model, store only the indices or references to the shared support vector storage.
- Adjust the model's prediction function to retrieve support vectors from the shared storage using the references.
sharedSupportVectors = containers.Map('KeyType', 'char', 'ValueType', 'any');
supportVectorIndices = cell(1, numModels);
model = trainSVM(X, Y(:, i));
supportVectors = model.SupportVectors;
for j = 1:size(supportVectors, 1)
sv = supportVectors(j, :);
if isKey(sharedSupportVectors, svKey)
index = sharedSupportVectors(svKey);
index = length(sharedSupportVectors) + 1;
sharedSupportVectors(svKey) = index;
indices(end + 1) = index;
supportVectorIndices{i} = indices;
model.SupportVectorIndices = indices;
Considerations:
- The above approach uses a map to store unique support vectors, which can be efficient for lookup operations.
- Ensure that the conversion of support vectors to string keys maintains numerical precision to avoid mismatches.
- Modify the prediction logic in your models to access the shared support vectors using the stored indices.
This approach allows you to reduce storage requirements by avoiding duplication of support vectors across models. Implementing this manually gives you full control over the process and can be tailored to your specific environment and constraints.
