Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling

• Published in: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 328 - 335
• Main Authors: Vangara, Raviteja, Skau, Erik, Chennupati, Gopinath, Djidjev, Hristo, Tierney, Thomas, Smith, James P., Bhattarai, Manish, Stanev, Valentin G., Alexandrov, Boian S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2020
• Subjects: Benchmark testing; Document clustering; Matrix decomposition; NLP; NMF; Noise measurement; Prediction algorithms; Predictive models; Semantics; Stability analysis; Topic models

Non-negative Matrix Factorization (NMF) models the topics of a text corpus by decomposing the matrix of term frequency-inverse document frequency (TF-IDF) representation, X, into two low-rank non-negative matrices: W , representing the topics and H, mapping the documents onto space of topics. One ch...