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Top 38 dimensionality-reduction open source projects

tGPLVM
tGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data
Unsupervised-Learning-in-R
Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
dbMAP
A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.
NIDS-Intrusion-Detection
Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN
UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation in Julia
Spectre
A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.
timecorr
Estimate dynamic high-order correlations in multivariate timeseries data
scHPF
Single-cell Hierarchical Poisson Factorization
tldr
TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses
50-days-of-Statistics-for-Data-Science
This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.
ParametricUMAP paper
Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).
DRComparison
Comparison of dimensionality reduction methods
sef
A Python Library for Similarity-based Dimensionality Reduction
partition
A fast and flexible framework for data reduction in R
mathematics-statistics-for-data-science
Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.
twpca
🕝 Time-warped principal components analysis (twPCA)
1-38 of 38 dimensionality-reduction projects