Let's say we have a data set with following features
[age, sex, country, city, annual income]
[35, male, USA, New York, 73000]
.
I came across the article which explains how to deal with strings as an NN input.
Article example showing how to standardize data:
[0] 30 male 38000.00 urban democrat
[1] 36 female 42000.00 suburban republican
[2] 52 male 40000.00 rural independent
[3] 42 female 44000.00 suburban other
[0] -1.23 -1.0 -1.34 ( 0.0 1.0) (0.0 0.0 0.0 1.0)
[1] -0.49 1.0 0.45 ( 1.0 0.0) (0.0 0.0 1.0 0.0)
[2] 1.48 -1.0 -0.45 (-1.0 -1.0) (0.0 1.0 0.0 0.0)
[3] 0.25 1.0 1.34 ( 1.0 0.0) (1.0 0.0 0.0 0.0)
Now let's try to use this approach in our example where we could end up with thousands of different cities. We would have to create at least N vectors size of [1 ... N]. That approach just does not seem to be very efficient when we have unlimited number of possibilities.
That was a brief explanation of standardizing data problem. Now let me expand it to my specific NN task. I have a data set with 30+ features. Features consist of real numbers, decimal numbers, strings. All of them are relevant.
I have to transform non-computable data into numeric, NN computable form. The question is how to standardize a set of data with many features of different types (strings and numbers)?