PCA: Principal Component Analysis (PCA).
In jlsuarezdiaz/rDML: Distance Metric Learning Algorithms for R
Description
Usage
Arguments
Value
Description
A distance metric learning algorithm for unsupervised dimensionality reduction, obtaining orthogonal directions that maximize the variance.
Usage
1
Arguments
num_dims
Number of components for dimensionality reduction. If NULL, all the principal components will be taken. Ignored if thres is provided. Integer.
Fraction
of variability to keep, from 0 to 1. Data dimension will be reduced until the lowest dimension that keeps 'thres' explained variance. Float.
Value
The PCA transformer, structured as a named list.
jlsuarezdiaz/rDML documentation built on May 24, 2019, 12:35 a.m.
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Description Usage Arguments Value
Description
A distance metric learning algorithm for unsupervised dimensionality reduction, obtaining orthogonal directions that maximize the variance.
Usage
1 |
Arguments
num_dims |
Number of components for dimensionality reduction. If NULL, all the principal components will be taken. Ignored if thres is provided. Integer. |
Fraction |
of variability to keep, from 0 to 1. Data dimension will be reduced until the lowest dimension that keeps 'thres' explained variance. Float. |
Value
The PCA transformer, structured as a named list.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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