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NMF (Non-negative Matrix Factorization)

ML Models & Algorithms
A topic modeling algorithm often compared to LDA and BERTopic, requiring text pre-processing and defining the number of topics beforehand.

NMF (Non-negative Matrix Factorization) is a topic modeling algorithm often compared to LDA and BERTopic. It is a classic clustering algorithm used for text clustering.
Similar to LDA, NMF requires that the number of topics must be specified by the user as a hyperparameter. It is suited for analyzing large or more structured text and can offer strong quantitative performance, although it often requires advanced tuning compared to newer embedding-based models.

Related Concepts

c-TF-IDF

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

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