svdProcessing: Dimensionality Reduction Via SVD and 3-D Plotting

In CAMCOS/camcos2017: CAMCOS Spring 2017: Efficient and Scalable Spectral Clustering for Document Clustering.

Description

Dimensionality Reduction Via SVD and 3-D Plotting

Usage

svdProcessing(data, n = 10, scale = 1, plot = FALSE, file = NULL)

Arguments

data	Matrix object. Accepts sparse or dense form.
n	Number of dimensions to be reduced to (default is 10)
scale	The sclae of axis, in case the data is very sparse. If data is too sparse, try 1000, ..., 10^6, etc.
plot	Logical. Return 3-D plots
file	Full path to filename without file type, which will be PNG.

Value

Matrix object with the same number of rows as the original input data, and a reduced number of columns equal to the input argument n.

CAMCOS/camcos2017 documentation built on May 6, 2019, 9:23 a.m.