Use multidimensional (n>2) array as input for train
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I would like to train a neural network for unsupervised clustering. I have 35 samples. Each sample is a 2-dimensional uint8-array, i.e. "input" is a < 35x1440x19 uint8 > array.
Using the sample from the "train" documentation I get:
% Create a Self-Organizing Map
dimension1 = 10;
dimension2 = 10;
net = selforgmap([dimension1 dimension2]);
% Train the Network
[net,tr] = train(net,input);
Error using nntraining.setup (line 13)
Inputs X is not two-dimensional.
Error in network/train (line 247)
[net,data,tr,err] = nntraining.setup(net,net.trainFcn,X,Xi,Ai,T,EW,true);
How do I convert "input" into a variable which "train" will accept? Any help would be appreciated.
2 个评论
Greg Heath
2014-2-26
This explanation makes no sense to me. The input should contain N I-dimensional vector examples with
[ I N ] = size(input);
Please explain what each of 35, 1440 and 19 represent.
Lorenzo
2014-2-26
采纳的回答
Greg Heath
2014-2-26
1 个投票
1. Convert the data to format long
2. Use PCA feature extraction to reduce IROW = 1440 to irow << IROW
3. Columnize using (:) to get I = 19*irow for the N = 35 examples
4. Nomenclature: The 35 examples constitute one sample
2 个评论
Image Analyst
2014-3-8
Lorenzo's "Answer" moved here:
Columnizing was a good advice, thanks. However, I think in this way some information will get missed (relation between the two dimensions). Dimension reduction (e.g. by PCA) is always a solution, however whether PCA is the right one might depend on the data type. Recution of the example size is good to avoid getting out of memory.
If I get it right then, the answer to my question would be: having examples with more than one dimenstion (i.e. samples with more than 2 dimensions) is not possible with "train", the dimensions must be reduced first?
Greg Heath
2014-3-8
Yes, and if the net is not a classifier, PCA should work
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