I am using a Neural Networks based classifier to run a classification for my data in n-dimensional.
Then I thought it may be a good idea to run dimension reduction like PCA for my data at first, and then put the PCA results into the classifier (I keep 3 PCs). However, the classification on the dimension reduced features are not as good as using the original high-dimensional features directly.
- If I use Neural Networks based classification (in Matlab), does it automatically do the dimension reduction for me?
- Should I run dimension reduction like PCA before running Neural Networks classification?
- Are there any other reasons the classification on PCA results is not as good as using the original high dimensional features?