alejandrods/Analysis-of-the-robustness-of-NMF-algorithms
Analysis of the robustness of non-negative matrix factorization (NMF) techniques: L2-norm, L1-norm, and L2,1-norm
This project helps researchers and data scientists understand the reliability of different Non-negative Matrix Factorization (NMF) techniques. It takes data, often from sources like image datasets, and provides insights into how well various NMF methods perform under noisy conditions. The output helps users choose the most robust NMF approach for tasks like feature selection or data reduction.
No commits in the last 6 months.
Use this if you are a researcher or data scientist evaluating which NMF algorithm will give you the most stable and accurate results when your data might be imperfect or noisy.
Not ideal if you need a plug-and-play NMF library for immediate application without wanting to understand the theoretical underpinnings or comparative robustness.
Stars
11
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 07, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/alejandrods/Analysis-of-the-robustness-of-NMF-algorithms"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
namkoong-lab/dro
A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
neu-autonomy/nfl_veripy
Formal Verification of Neural Feedback Loops (NFLs)
THUDM/grb
Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for...
MinghuiChen43/awesome-trustworthy-deep-learning
A curated list of trustworthy deep learning papers. Daily updating...
ADA-research/VERONA
A lightweight Python package for setting up robustness experiments and to compute robustness...