Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective
Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective
Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each element measures the similarity between data points. Most existing symmetric NMF algorithms require factor matrices to be nonnegative, …