This is my data,you can see the 1st,2nd,40th and another features has different distribution law.
my data is a matrix, there is one box per column.On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and outliers are plotted individually.
I want to use this as input and output of an LSTM anto-encoder.
So how to normalize the data?
I try mean-std and find it better than min-max rule(normalize the data to [0,1]) when feeding into LSTM.
But it's still a not very perfect rule to fit this problem.