Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
This work addresses efficient symNMF for data analysis, particularly clustering, but appears incremental as it builds on existing coordinate descent methods.
The paper tackles the problem of symmetric nonnegative matrix factorization (symNMF) for clustering tasks by proposing coordinate descent schemes, showing favorable performance compared to state-of-the-art methods on synthetic and real-world datasets.
Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix $H$, usually with much fewer columns than $A$, such that $A \approx HH^T$. SymNMF can be used for data analysis and in particular for various clustering tasks. In this paper, we propose simple and very efficient coordinate descent schemes to solve this problem, and that can handle large and sparse input matrices. The effectiveness of our methods is illustrated on synthetic and real-world data sets, and we show that they perform favorably compared to recent state-of-the-art methods.