mims-harvard/fastGNMF
Fast graph-regularized matrix factorization
This project helps machine learning engineers and researchers quickly perform graph-regularized non-negative matrix factorization. It takes a dataset as input and outputs two matrices that represent a lower-dimensional, factored version of the original data. This technique is particularly useful for tasks like feature extraction or clustering, allowing users to efficiently analyze complex data structures.
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Use this if you are a machine learning practitioner working with large datasets and need to efficiently perform non-negative matrix factorization with graph regularization.
Not ideal if you are looking for a high-level, no-code solution for data analysis or if you are unfamiliar with Python programming and machine learning concepts.
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20
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5
Language
Python
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Last pushed
Oct 03, 2023
Commits (30d)
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