Does it make sense to combine PCA with an artificial neural network? I have a Dataset of around 200 features. Most of them are categorical and only a few are numerical. It seems that an artificial neural network with an Autoencoder has some problems with that kind and amount of features. Therefore, I thought to use PCA to reduce the dimensions and then apply the Autoencoder afterwards. 

Does the combination of a PCA before neural nets makes sense as the neural nets also reduces the information in internal layers? Does anybody has experiences with such a combination?



Edit: There is also an interesting answer in the stats forum.
Neural networks are actually extremely effective at performing dimensionality reduction. A great example is word2vec, which applies a shallow neural network to reduce inputs on the order of several million features (i.e. unnormalized text) to a 30-150 dimensional vector via a process that is mathematically analogous to matrix factorization (which is the class of techniques PCA belongs to). Autoencoders function very similarly to word2vec: if you're planning on using an autoencoder to learn an embedding of your data, I wouldn't expect you'd gain anything from applying PCA first rather than using an autoencoder (or something similar) to learn a better embedding for your data that isn't constrained by the assumptions of PCA.