| SVD_PSSM {PSSMCOOL} | R Documentation |
Singular Value Decomposition (SVD)
Description
Singular value decomposition is a general purpose matrix factorization approach
that has many useful applications in signal processing and statistics. In this function SVD is
applied to a matrix representation of a protein with the aim of reducing its dimensionality
Given an input matrix Mat with dimensions N*M SVD is used to calculate its factorization
of the form: Mat=U\Sigma V, where \Sigma is a diagonal matrix whose diagonal
entries are known as the singular values of Mat. The resulting descriptor is the ordered
set of singular values: SVD\in\mathcal{R}^L, where L=min(M,N).
and here svd function is used for this purpose.
Usage
SVD_PSSM(pssm_name)
Arguments
pssm_name |
name of PSSM Matrix file |
Value
feature vector of length 20
References
L. Nanni, A. Lumini, and S. J. T. S. W. J. Brahnam, "An empirical study of different approaches for protein classification," vol. 2014, 2014.
Examples
X<-SVD_PSSM(system.file("extdata", "C7GQS7.txt.pssm", package="PSSMCOOL"))