How is a non-negative matrix factorization used?

How is a non-negative matrix factorization used?

Non-Negative Matrix Factorization is a statistical method to reduce the dimension of the input corpora. It uses factor analysis method to provide comparatively less weightage to the words with less coherence. For a general case, consider we have an input matrix V of shape m x n.

When do you use NMF for topic modeling?

NMF by default produces sparse representations. This mean that most of the entries are close to zero and only very few parameters have significant values. This can be used when we strictly require fewer topics. NMF produces more coherent topics compared to LDA.

When is L1 regularization added to NMF it is called non negative sparse coding?

When L1 regularization (akin to Lasso) is added to NMF with the mean squared error cost function, the resulting problem may be called non-negative sparse coding due to the similarity to the sparse coding problem, although it may also still be referred to as NMF.

Which is the best method to perform NMF?

The other method of performing NMF is by using Frobenius norm. It is defined by the square root of sum of absolute squares of its elements. It is also known as eucledian norm. The formula and its python implementation is given below.

How is the input matrix used in NMF?

In the document term matrix (input matrix), we have individual documents along the rows of the matrix and each unique term along the columns. In case, the review consists of texts like Tony Stark, Ironman, Mark 42 among others. It may be grouped under the topic Ironman.

How is generalized Kullback-Leibler divergence used in NMF?

1. Generalized Kullback–Leibler divergence It is a statistical measure which is used to quantify how one distribution is different from another. Closer the value of Kullback–Leibler divergence to zero, the closeness of the corresponding words increases. In other words, the divergence value is less.

Which is the best definition of NMF machine learning?

As mentioned earlier, NMF is a kind of unsupervised machine learning. The main core of unsupervised learning is the quantification of distance between the elements. The distance can be measured by various methods. Some of them are Generalized Kullback–Leibler divergence, frobenius norm etc. 1. Generalized Kullback–Leibler divergence