dimreduce: Dimension reduction for supervised learning

In jpiironen/dimreduce: Supervised Dimension Reduction

Description

dimreduce is an R package that provided functions for (supervised) dimension reduction.

Functions

spca, ispca

Supervised PCA (SPCA) and iterative supervised PCA (ISPCA), that are useful techniques for dimension reduction. spca can also be used to compute the standard unsupervised PCA.

coef_transform

Function that can be used to map linear model regression coefficients fitted using latent features from SPCA or ISPCA back to the original feature space. This can be useful for analysing the model.

featscore, featscore.test

Functions for computing univariate relevance scores and significance tests for the features, which can be used for screening.

References

Bair, E., Hastie, T., Paul, D., and Tibshirani, R. (2006). Prediction by supervised principal components. Journal of the American Statistical Association, 101(473):119-137.

Neal, R. and Zhang, J. (2006). High dimensional classification with Bayesian neural networks and Dirichlet diffusion trees. In Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A., editors, Feature Extraction, Foundations and Applications, pages 265-296. Springer.

Piironen, Juho and Vehtari, Aki (2018). Iterative supervised principal components. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) PMLR 84: 106-114.

jpiironen/dimreduce documentation built on March 18, 2021, 11:52 p.m.