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When I have N samples of 2 different features (different scale, different meaning etc.), but whose values are scalar numbers of the same format.

Does it make sense to flatten the data into a 1D array of length [N*2] and feed it in this 1D shape into the neural network. Or must I keep them separate (as columns or otherwise)? I will standardize each feature before combining them into the single array.

(I want to do this for convenience reasons i.e. not deal with multidimensional arrays).

The data is input for a classification problem.

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  • $\begingroup$ Can you be more specific about your classification problem? Are you supposed to classify each sample separately? Also, note that not wanting to deal with multi-dimensional arrays for convenience is unwise and, in my opinion, not a good excuse. You might be needing to improve your grasp of ND arrays in your language of choice for this (separately, of course). $\endgroup$ – E_net4 the flagger Aug 10 '17 at 9:15
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I believe it does, for example, if you flatten both features into 1-D array and then concatenate them, they should still preserve most of the information. Because total number of feature vector still remains the same.

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