dCUR:Dimension Reduction with Dynamic CUR
Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying
the k, the number of columns and rows used, and its final
purposes to help find the stage, which minimizes the relative
error to reduce matrix dimension. The goal of CUR Decomposition
is to give a better interpretation of the matrix decomposition
employing proper variable selection in the data matrix, in a
way that yields a simplified structure. Its origins come from
analysis in genetics. The goal of this package is to show an
alternative to variable selection (columns) or individuals
(rows). The idea proposed consists of adjusting the probability
distributions to the leverage scores and selecting the best
columns and rows that minimize the reconstruction error of the
matrix approximation ||A-CUR||. It also includes a method that
recalibrates the relative importance of the leverage scores
according to an external variable of the user's interest.
