Guided Semi-Supervised Non-Negative Matrix Factorization

Algorithms, 2022

Recommended citation: Li, P.; Tseng, C.; Zheng, Y.; Chew, J.A.; Huang, L.; Jarman, B.; Needell, D. Guided Semi-Supervised Non-Negative Matrix Factorization. Algorithms 2022, 15:136 Online access

We present a novel Guided Semi-Supervised Non-negative Matrix Factorization (GSSNMF) that incorporates class labels and seed features to perform both classification and clustering. We evaluate the performance of GSSNMF for these two tasks using the 20 Newsgroups dataset, as well as text data from the California Innocence Project.