Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

Fast truncated singular value decompositions

Randomized Matrix Decompositions using R

Imputation method for scRNA-seq based on low-rank approximation

MoMA: Modern Multivariate Analysis in R

Window based randomized singular value decomposition (winSVD)

Matrix completion algorithms, e.g. for recommender systems or predictions.

Low-rank Singular Value Decomposition (SVD) and Soft Impute Algorithm for matrix completion

Implementation of machine learning algorithms in R Programming language

This repository provides code in R for the computer vision problem of human face recognition.

A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).

‘linr’ is used to fit a linear model with high efficiency. It is implemented by algorithm of three matrix decomposition methods, QR decomposition, Cholesky decomposition and the singular value decomposition (SVD).

Factor Analysis using PCA, SVD and BNN autoencoders

This projects focusses on PCA based Statistical Visualization for multivariate large data sets. Popular methods for visualizing PCA transformations are performed in R.

Analysis of multi-block supervised problems thanks to SVD-based methods permitting variable selection.

Re-sampled dimensional reduction (RSDR)

MultivariateAnalysis using R