Per the suggestion by @robmaz, RSpectra::svds() now has two new parameters center and scale, to support implicit centering and scaling of matrices in partial SVD. The minimum version for this new feature is RSpectra >= 0.16-0.
These two parameters are very useful for principal component analysis (PCA) based on the covariance or correlation matrix, without actually forming them. Below we simulate a random data matrix, and use both R’s built-in prcomp() and the svds() function in RSpectra to compute PCA.
In January 2016, I was honored to receive an “Honorable Mention” of the John Chambers Award 2016. This article was written for R-bloggers, whose builder, Tal Galili, kindly invited me to write an introduction to the rARPACK package.
A Short Story of rARPACK Eigenvalue decomposition is a commonly used technique in numerous statistical problems. For example, principal component analysis (PCA) basically conducts eigenvalue decomposition on the sample covariance of a data matrix: the eigenvalues are the component variances, and eigenvectors are the variable loadings.
This semester I’m taking a course in big data computing using Scala/Spark, and we are asked to finish a course project related to big data analysis. Since statistical modeling heavily relies on linear algebra, I investigated some existing libraries in Scala/Java that deal with matrix and linear algebra algorithms.
1. Set-up Scala/Java libraries are usually distributed as *.jar files. To use them in Scala, we can create a directory to hold them and set up the environment variable to let Scala know about this path.