I am working with a dataset where each elements is a square table of size m-by-n, where m (the number of rows) is the same for all the data points, while n (the number of columns) varies from tens to thousands. I need to classify the elements of this dataset in two or more clusters (or alternatively, determine outlier/atypical elements.)
What I am looking for is:
- either ML/statistical algorithms adapted for working with such disparate size datasets
- or typical transformations reducing the elements of the dataset to a common feature space (one possibility is calculating correlations of rows, thus representing each element by an m-by-m matrix.)
I apologize, if the question is too vague or not suitable for this community. I would however appreciate any tips/ideas.