I have a classification problem with the following characteristics:
- a few million data points
- around one hundred features
- non-linearly separable Training a SVM with an RBF Kernel is not feasible because of the size of the data set.
My idea is the following:
- reduce the size of the data set to a few thousand points by applying K-Means clustering
- transform the data set by replacing all the features with the the similarities between the original points and the centroids of the clusters
- train a linear SVM on the new dataset
Does this approach sound reasonable from a mathematical perspective?
What other classification algorithms do you recommend for large data sets that are not linearly separable?
kerneltag and added
dimensionality-reductiontag to your question since it appears more relevant. $\endgroup$