问题
suppose i have 10 individual observations each of size (125,59). i want to group these 10 observations based on their 2d feature matrices ((125,59)).Is this possible without flattening every observation to 125*59 1D matrix ? I cant even implement PCA or LDA for feature extraction because the data is highly variant. Please note that i am trying to implement clustering through self organizing maps or neural networks. Deep learning and neural networks are completely related to the question asked.
回答1:
Of course it is.
Define an appropriate distance measure.
Then compute the 10x10 distance matrix, and run hierarchical clustering.
来源：https://stackoverflow.com/questions/54815040/how-to-do-clustering-when-the-shape-of-data-is-x-y-z