A flexible and efficient С++ template library for dimension reduction

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

A fast xgboost feature selection algorithm

Deep learning meets molecular dynamics.

Randomized Dimension Reduction Library

Randomized Matrix Decompositions using R

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

A header-only C++ library for sketching in randomized linear algebra

Fast k-Nearest Neighbors Classifier for Large Datasets

Sparse Principal Component Analysis (SPCA) using Variable Projection

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

Dimension Reduction and Estimation Methods

A fast, scalable and light-weight C++ Fréchet and DTW distance library, exposed to python and focused on clustering of polygonal curves.

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

Dimension reduced surrogate construction for parametric PDE maps

Reconstruction and Compression of Color Images Using Principal Component Analysis (PCA) Algorithm

Statistical quality evaluation of dimensionality reduction algorithms

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

manage ordinations and render biplots in a tidyverse workflow