lanl/T-ELF
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
This project helps scientists, analysts, and researchers uncover hidden structures and patterns within large, complex datasets from fields like biology, chemistry, and text mining. It takes in raw multi-dimensional data, cleans it, and automatically extracts key features and relationships, presenting them in a structured way to support better decision-making. Researchers across various scientific disciplines, including material science and climate studies, would find this valuable.
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Use this if you need to automatically discover underlying themes, groupings, or connections in large, multi-faceted datasets, without needing to pre-specify how many features you expect to find.
Not ideal if you are working with small datasets or only need basic statistical analysis rather than advanced pattern extraction.
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Language
Python
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Last pushed
Oct 08, 2025
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