# Number of inputs much greater than size of training (Neural Networks)

I'm working with neural networks.

I have a number of inputs to the network which is far greater than the number of training instances ($\sim 7k$ inputs vs $700$ samples, $1$ order of magnitude, number of outputs $2k$).

My question, as a newcomer to neural nets, is this:

Should I diminish the size of the input layer (maybe through an autoencoder) ?

Dimensional reduction was suggested to me, but I didn't really understand why.

• Sorry for bothering you, it's not that clear to me one thing: are you saying that the number of inputs ($7k$ in my case) is not necessarily equal to the number of features one extract from those inputs? Or are you plainly saying that a high number of inputs/features is not an issue whichever the number of data? I'm forecasting $2k$ products daily sales. For now I'm considering $340$ products (read output) and a corresponding $\sim 1k$ inputs. Forecasting performance minimizing WeightedMape out performs every other algorithm, so I was doubting the suggestion given to me Feb 16, 2017 at 16:06