What is Singular Value Decomposition (SVD) and how does it work?
Singular value decomposition (SVD) is one of the jewels of linear algebra. In modern times it has found applications in machine learning and Artificial intelligence. The most important feature of SVD is that of dimension reduction so that we are able to make predictions on very large data sets with a small subspace of variables. Let’s try to understand how it works through a real-life example. Netflix has many subscribers and they have a collection of movies. When we watch a movie, we often see similar movies being recommended to us next time we go to Netflix and we wonder how did they figure out the kind the movies I like! The answer is SVD . As an example, suppose Netflix has 5 movies and multiple subscribers. Netflix gets the data for every user with a rating going from 0 to 5 with 0 implying the user did not watch a particular movie and 5 implying the user has watched it multiple times. A sample data set will look like this. Each row in this matrix A will cor