Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
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Updated
Jul 3, 2024 - R
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
Matrix completion algorithms, e.g. for recommender systems or predictions.
Implementation of machine learning algorithms in R Programming language
Low-rank Singular Value Decomposition (SVD) and Soft Impute Algorithm for matrix completion
‘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).
A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).
This repository provides code in R for the computer vision problem of human face recognition.
Analysis of multi-block supervised problems thanks to SVD-based methods permitting variable selection.
Re-sampled dimensional reduction (RSDR)
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.
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