A detailed overview of this workflow from end-to-end can be found at the following link:
1. The first step here is to calculate the transform model using either the 'rica' or 'sparsefilt' functions. Examples below:
>> myModel = rica( myData , newDimension );
or
>> myModel = sparsefilt( myData , newDimension );
2. After this, you can transform your data into the new space:
>> transformedData = myModel.transform( myData );
For more information (and examples) about using these two functions please refer to the following links:
3. Now that your data is ready for training, you can train a Classification Tree using the 'fitctree' function:
>> myTree = fitctree( transformedData , labels );
4. Finally you are ready to create predictions on some other (transformed) dataset:
>> predicatedClasses = myTree.predict( myModel.transform(myOtherData) );
For more information on 'fitctree', you can refer to the following link:
Note that 'fitctree' has very many options (link below) and parameters that can be set, as seen below:
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