Metadata-Version: 2.1
Name: SupervisedMF
Version: 0.0.2
Summary: A package for various supervised matrix factorization methods
Author-email: Agam Goyal <agamg2@illinois.edu>, Yi Wei <ywei224@wisc.edu>, Hanbaek Lyu <hlyu@math.wisc.edu>
Maintainer-email: Agam Goyal <agamg2@illinois.edu>
Project-URL: Homepage, https://github.com/pypa/sampleproject
Project-URL: Issues, https://github.com/pypa/sampleproject/issues
Keywords: supervised matrix factorization,matrix factorization,dimensionality reduction,low-rank compression,classification
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=2.0.0
Requires-Dist: scipy >=1.13.0
Requires-Dist: scikit-learn >=1.5.1
Requires-Dist: matplotlib >=3.9.0
Requires-Dist: pandas >=2.2.2
Requires-Dist: seaborn >=0.13.0
Requires-Dist: tqdm >=4.66.4

# Supervised Matrix Factorization

This Python package contains source codes for algorithms for Supervised Matrix Factorization (SMF) in the following papers: 

[1] Joowon Lee, Hanbaek Lyu, Weixin Yao
[*"Exponentially Convergent Algorithms for Supervised Matrix Factorization*"](https://papers.nips.cc/paper_files/paper/2023/hash/f2c80b3c9cf8102d38c4b21af25d9740-Abstract-Conference.html) (NeurIPS 2023)

[2] Joowon Lee, Hanbaek Lyu, Weixin Yao
[*"Supervised Matrix Factorization: Local Landscape Analysis and Applications*"](https://arxiv.org/abs/2102.06984) (ICML 2024)
