What is NMF in machine learning?

Abstract. In this chapter we introduce the Non-Negative Matrix Factorization (NMF), which is an unsupervised algorithm that projects data into lower dimensional spaces, effectively reducing the number of features while retaining the basis information necessary to reconstruct the original data.

What is NMF algorithm?

Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements.

Is NMF deterministic?

Non-negative matrix factorization (NMF) has proven to be a useful decomposition technique for multivariate data, where the non-negativity constraint is necessary to have a meaningful physical interpretation. The NMF algorithm, however, assumes a deterministic framework.

Why NMF is non negative?

Almost all NMF algorithms use a two-block coordinate descent scheme (exact or inexact), that is, they optimize alternatively over one of the two factors, W or H, while keeping the other fixed. The reason is that the subproblem in one factor is convex. More precisely, it is a nonnegative least squares problem (NNLS).

What is dimension reduction and why it is important?

Dimensionality reduction is extremely useful for data visualization — When we reduce the dimensionality of higher dimensional data into two or three components, then the data can easily be plotted on a 2D or 3D plot. To see this in action, read my “Principal Component Analysis (PCA) with Scikit-learn” article.

Why is Dimension Reduction important?

It reduces the time and storage space required. It helps Remove multi-collinearity which improves the interpretation of the parameters of the machine learning model. It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.