- PyMF: Python Matrix Factorization Module
- CIKM 2010 paper accepted
- CIG 2010 paper accepted + tutorial + special session
- ICML 2010 Workshop on Machine Learning and Games
- ICPR 2010 papers
- Paper selected as one of the best at ICDM 2009
- Corpse Advertisement in World of Warcraft
- ICDM 2009 paper accepted
- ICMI 2009 paper accepted
- ICVSS 2009 Summer School
Software
PyMF
Python Matrix Factorization (PyMF) is a module for several constrained/unconstrained matrix factorization (and related) methods. The module is early alpha and not very well tested. It can be downloaded from this page http://pymf.googlecode.com/.
PyMF currently includes the following methods:
- Non-negative matrix factorization (NMF)
- Convex non-negative matrix factorization (CNMF)
- Semi non-negative matrix factorization (SNMF)
- Archetypal analysis (AA)
- Simplex volume maximization (SiVM)
- Convex-hull non-negative matrix factorization (CHNMF)
- Binary matrix factorization (BNMF)
- Singular value decomposition (SVD)
- Principal component analysis (PCA)
- K-means clustering (Kmeans)
- CUR decomposition (CUR)
- Compaxt matrix decomposition (CMD)
CH-NMF - Python
Python implementation of convex hull non-negative matrix factorization (CH-NMF) (have a look at the paper) and other variants of non-negative matrix factorization (requires cvxopt ). Code available here. [PyMF is more recent and also contains methods for CH-NMF]