Description Usage Arguments Value References Examples
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.
1 | SVD_PSSM(pssm_name)
|
pssm_name |
name of PSSM Matrix file |
feature vector of length 20
L. Nanni, A. Lumini, and S. J. T. S. W. J. Brahnam, "An empirical study of different approaches for protein classification," vol. 2014, 2014.
1 | X<-SVD_PSSM(system.file("extdata", "C7GQS7.txt.pssm", package="PSSMCOOL"))
|
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