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I think the problem can be formulated as a clustering of data into 2 categories (stressed, not stressed) (see answer to question in this q&a entry) -- main difference with that situation is that here we know how many clusters to create. The openCV method for clustering in N-dimension is cvKMeans2. The method is quite easy to use, just being careful to create a matrix where each row contains a sample vector, in this case the 9 points mentioned.

The results would be optimal if the underlying distributions are concentrated, i.e. there are indeed two states to distinguish. Computationally speaking k-Means is fast because you identify a termination criteria via number of iterations and/or solution accuracy; the larger the gradient the faster it would converge, and the gradient . LDA or similar approaches rely on creating a big matrix description of the system, then inverting the matrix or solving the large linear system at once, vs. the iterative approach of the k-Means algorithm.

I think the problem can be formulated as a clustering of data into 2 categories (stressed, not stressed) (see answer to question in this q&a entry) -- main difference with that situation is that here we know how many clusters to create. The openCV method for clustering in N-dimension is cvKMeans2. The method is quite easy to use, just being careful to create a matrix where each row contains a sample vector, in this case the 9 points mentioned.

The results would be optimal if the underlying distributions are concentrated, i.e. there are indeed two states to distinguish. Computationally speaking k-Means is fast because you identify a termination criteria via number of iterations and/or solution accuracy; the larger the gradient the faster it would converge, and the gradient . is basically the concentration of samples. LDA or similar approaches rely on creating a big matrix description of the system, then inverting the matrix or solving the large linear system at once, vs. the iterative approach of the k-Means algorithm.