R package rCUR: CUR decomposition package. Functions and objects for CUR matrix decomposition. In modern data mining tasks the user has often matrices with huge number of rows and/or columns as the base of analysis. One way of the analysis of this type of huge datasets is to reduct their dimensions. Principal component analysis (PCA) is a widely used tool for such data analysis. PCA produced singular vectors are mathematical abstractions and hardly interpretable on the field from which the data are drawn. Mahoney & Drineas (2009) proposed a method the CUR matrix decomposition what decreases the dimensions as well, but the resulting matrices are interpretable on the application area. This package contains functions and objects to help doing CUR matrix decomposition.
Keywords for this software
References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
- Voronin, Sergey; Martinsson, Per-Gunnar: Efficient algorithms for CUR and interpolative matrix decompositions (2017)
- Saibaba, Arvind K.: HOID: higher order interpolatory decomposition for tensors based on Tucker representation (2016)