What does NMF stand for in the context of topic modeling?

Non-Negative Matrix Factorization (NMF) is a powerful technique used in topic modeling, particularly for discovering latent structures within data. In this context, NMF decomposes a given matrix into two lower-dimensional matrices, often interpreted as the features and coefficients. The fundamental requirement of non-negativity means that the matrix factors can only contain zero or positive values, which is particularly useful for applications like topic modeling because it aligns well with the idea that counts of words or terms in text data are inherently non-negative.

By applying NMF in topic modeling, one can extract a set of topics from a collection of documents. Each document is represented as a combination of these topics, captured in a way that enhances interpretability. Since the resulting matrices are composed of non-negative entries, they can be more easily understood as representing probabilities or proportions, which is a common requirement in text analysis scenarios.

Other options, such as Neural Matrix Framework, Natural Model Formation, and Numeric Matrix Formation, do not accurately reflect established terms or methods used in the realm of topic modeling, and they do not convey the same implications of the methodology as NMF does. Non-Negative Matrix Factorization stands out as a well-recognized technique, particularly in fields that analyze textual