"dean86" wrote in message <email@example.com>... > Hi all, > I have the following problem: > 777 input from some different sensors, I have to do a PCA and then a RBF to predict the number of analytes.
What type of sensors? How many measurement vectors for NN input? N = 343? How many dimensions in each input vector? I = 777 ? Regression or classification ? What is the output ? How many dimensions in each output vector? O = ?
> Could you help me to understand the correct way to do the RBF, to use the command 'newrb', but above all, how to understand the results?
> First thing that I do is to load the data, then I have this matrix 777x343, so I do the transpose and I start to do the mean-centring and then the PCA on this matrix and I obtain the scores (343x4) and the loadings (777x4). Now I have to use this scores to do this RBF, so I obtain the transpose of the scores matrix (4x343) and now, should I use the newrb with this last matrix and the original data matrix (777x343)? > What is the criterion for input dimensionality reduction why 777==> 4? This makes no sense to me.
All NEWRB needs are input and output matrices and a reasonable value for the MSE goal and a range of candidate spread values.
For regression standarize both input and target matrices. For c-class classification, standardize the input matrix but use one of c (=O) binary coding for the output matrix.
Use MSEgoal = 0.01*mean(var(t',1)) % yields R^2 >= 0.99
Obtain multiple designs from a loop over spread values. I usually start with a coarse search spread = 2^(i-1), i = 1,2,... Then refine the search if needed.
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