MATLAB: Feature Selection by NCA for an SVM classifier

Question

Hi

Apparently, 'fscnca' is using a model that is built into a nearest neighbour (NN) classifier. That means the feature weights are calculated based on the performance of a NN classifier. My question is will 'fscnca' be a suitable feature selection tool if one is using another type of classifier such as SVM? MATLAB tutorials has not put such a restriction on using 'fscnca'.

Cheers, Roohollah

Answer 1

Hi Roohollah. Technically, there is no guarantee that the feature selection in fscnca will be applicable to an SVM. As you touched on, classifiers like SVMs and k-means are fundamentally different. However, in practice, the "importance" of features can often be generalized, especially if your data can be mapped well to classification by both algorithms. Feature selection with fscnca will most likely be better than no feature selection at all. In fact, the documentation has an example using fitcsvm on features obtained from fscnca:

http://www.mathworks.com/help/stats/fscnca.html?s_tid=doc_ta#zmw57dd0e377743

The Statistics and Machine Learning Toolbox has a variety of functions for both feature extraction and dimensionality reduction:

http://www.mathworks.com/help/stats/dimensionality-reduction.html

I would encourage you to try out various approaches and see what works best for you and your specific data.