rCUR: an R package for CUR matrix decomposition
نویسندگان
چکیده
منابع مشابه
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The standard sketch based method, which we call the prototype model, produces relatively accurate approximations, but is inefficient on large square matrices. The Nyström method is highly efficient, but can only achieve low a...
متن کاملTowards More Efficient Nystrom Approximation and CUR Matrix Decomposition
Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The sketching based method, which we call the prototype model, produces relatively accurate approximations. The prototype model is computationally efficient on skinny matrices where one of the matrix dimensions is relatively ...
متن کاملImproving CUR Matrix Decomposition and Nyström Approximation via Adaptive Sampling
The CUR matrix decomposition and Nyström method are two important low-rank matrix approximation techniques. The Nyström method approximates a positive semidefinite matrix in terms of a small number of its columns, while CUR approximates an arbitrary data matrix by a small number of its columns and rows. Thus, the CUR decomposition can be regarded as an extension of the Nyström method. In this p...
متن کاملSparseM: A Sparse Matrix Package for R
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.
متن کاملRelative-Error CUR Matrix Decompositions
Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of “components.” Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data. In this paper, we propose and study matrix approximations that are explicitly express...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2012
ISSN: 1471-2105
DOI: 10.1186/1471-2105-13-103