readme.Md

In jimeharrisjr/svdtools: SVD Tools for Matrices

svdtools

SVDTOOLS - tools for reducing and filtering matrices

Single Value Decomposition is a useful tool for the analysis and filtering of matrices. I regularly use these SVD functions for things like:

• Reducing noise in images (with the imager package, for example)
• Filtering collections of signal trace spectra to remove noise
• Preventing overfitting in tensorflow models with limited training data

Installing

Install svdtools from Github using devtools:

devtools::install_github('https://github.com/jimeharrisjr/svdtools')

Usage Examples

Single Value Decomposition can remove noise from matrices in the process of reducing them. One example is image filtering. The included data object noisymatrix is one such example. This is a grayscale image of Linux Penguin on Wikimedia Commons from Larry Ewing [e-mail]. At random, some pixels have been deleted, creating noise.

library(svdtools)
# show the noisy image
image(noisymatrix, col=gray.colors(65536))
# Plot the explanation
plot_explanation(noisymatrix, limit=.99)

There are a lot of components to play with. One brute force way of doing this would be to try specifying some percentage of components to keep. We could try 98%:

m<-reduce_percentage(noisymatrix, .98)
image(m, col=gray.colors(65536))

This image is obviously overfiltered. Instead, we could simply eliminate all the components above some threshold - for example, discard 51-411:

# Keep the first 50 components
m<-reduce_components(noisymatrix, 50)
image(m, col=gray.colors(65536))

See the vignettes for more detail, and for signal filter examples.

jimeharrisjr/svdtools documentation built on Jan. 7, 2021, 12:47 a.m.