I would like to identify features in this image. In the example above I have identified one feature I would like to automatically recognize. There are others I would like to identify. In this example, the data in all four vectors moves up at the same time, however this can happen at different rates and sometimes it can be delayed.
My regular set of tools is inadequate for this type of problem. I have no problem detecting peaks or using thesholding. This is the first time I've had to do this with multi dimensional data. The other problem is the data is very noisy so peak detection and thresholding yeilds false positives.
I would like to plug this into a neural network and classify it, but the problem is in this 4x3000 vector I might only have 4 or 5 examples of features that I could train with. So it might be hard to train a CNN if I don't have many examples.
There are many tools that work with 1x(n col) vectors, but I need to detect patterns that coincide in all four vectors. Another problem I have is
How can I compare a 4x5 section of the 4x3000 matrix and find similarities?
The think I would really like to do is take a 4x5 matrix and compare it with the rest of the vector and see if and where it matches (so really there would be 2995 matrices of size 4x5). Are there neural networks that can make multidimensional comparisons like this?