Nmf regularization

1. What is the difference between NMF and PCA?
2. What is NMF used for?
3. What is NMF in NLP?
4. What is an NMF model?

What is the difference between NMF and PCA?

It shows that NMF splits a face into a number of features that one could interpret as "nose", "eyes" etc, that you can combine to recreate the original image. PCA instead gives you "generic" faces ordered by how well they capture the original one.

What is NMF used for?

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors.

What is NMF in NLP?

Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training a high-quality topic model requires large amount of textual data.

What is an NMF model?

Non-Negative Matrix Factorization (NMF) is an unsupervised technique so there are no labeling of topics that the model will be trained on. The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation.