The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
This is the last article in a series about the nonnegative matrix factorization (NMF). In this article, I run and visualize an NMF analysis of the Scotch whisky data and compare it to a principal component analysis (PCA). Previous articles in the series provide information about the whisky data, the
The classical singular value decomposition (SVD) has a long and venerable history. I have described it as a fundamental theorem of linear algebra. Nevertheless, the mathematical property that makes it so useful in general (namely, the orthogonality of the matrix factors) also makes it less useful for certain applications. In
A common task in statistics is to approximate a large data matrix by using a low-rank approximation. Low-rank approximations are used for reducing the dimension of a problem (for example, in principal component analysis), for image analysis via the singular value decomposition (SVD), and for decomposing large matrices that are