I have 25 matrices 19x19 containing coherence measure between EEG electrodes. I want to divided them into some groups by clustering or any other method. I know how to deal with vectors, but I can't find anything about clustering of set of matrices. If it can help - I think we can use coherence as a distance between cells into matrix. So may be there are some method for clustering of distance matrices?
Traditional clustering methods cluster vectors. In the vector space, the distance metric and other distance functions are well defined. The Euclidean distance between vectors $x_1$ and $x_2$ is $|x_1-x_2|_2$, the 2-norm of $x_1-x_2$. Analogously, to compare two matrices $M_1$ and $M_2$, we may want to compute $|| M_1-M_2||_p$, the p-norm of $M_1-M_2$.
While there are two solution:
- Compute distance between matrices as sum of squares of the difference matrix
- Unwrap a triangle of each matrix into a vector
Is there something else?