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.
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Nikhila Munipalli
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Question is quite unclear, and has nothing to do with `neural-network` or `deep-learning` - kindly do not spam irrelevant tags (removed). – desertnaut Feb 21 '19 at 21:22
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Of course it is.
Define an appropriate distance measure.
Then compute the 10x10 distance matrix, and run hierarchical clustering.
Has QUIT--Anony-Mousse
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I can't. Defining an appropriate distance measure is your task - it's your data and problem, you have to know yourself what you want to be similar. – Has QUIT--Anony-Mousse Feb 24 '19 at 17:51