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

A fast xgboost feature selection algorithm

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.

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

Dimension reduced surrogate construction for parametric PDE maps

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

A novel method for single-cell diagonal integration: scConfluence

sliced: scikit-learn compatible sufficient dimension reduction

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

Repository for the AugmentedPCA Python package.

NTUEE 2018 spring course - Machine Learning (Pei-Yuan Wu, Hung-Yi Lee, Tsungnan Lin)

An interactive toolkit for visualizing GMM convergence in 3D/2D, featuring PCA for dimensionality reduction, K-means++ initialization, and covariance regularization for stability.

Visualize the Latent Space of an Autoencoder using matplotlib

An Explainable Deep Network for Dimension Reduction

A Python package for dimension reduction and evaluation workflows with CyTOF data.

Protein Sequence Embedding in Complex Space

This is a repository for the paper "Contrastive Multiple Correspondence Analysis (cMCA): Applying the Contrastive Learning Method to Identify Political Subgroups."

Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing: we investigate the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use. In addition, we propose a novel supervision approach in which the model uses its own predictions of the lab…

[ECML-PKDD 2021] Invertible Manifold Learning for Dimension Reduction

Implementation of the most basic SDR (sufficient dimension reduction) methods in Python