SVD_PSSM: Singular Value Decomposition (SVD)

In PSSMCOOL: Features Extracted from Position Specific Scoring Matrix (PSSM)

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=UΣ V, where Σ 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"))

PSSMCOOL documentation built on Jan. 4, 2022, 5:07 p.m.