101 Open Source Dimensionality Reduction Software Projects
Free and open source dimensionality reduction code projects including engines, APIs, generators, and tools.
Tirthajyoti Machine Learning With Python 1600 ⭐
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Awesome Single Cell 1498 ⭐
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Awesome Community Detection 1427 ⭐
A curated list of community detection research papers with implementations.
Siamesenetwork Tensorflow 245 ⭐
Using siamese network to do dimensionality reduction and similar image retrieval
Multivariatestats.jl 191 ⭐
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
Benedekrozemberczki Datasets 163 ⭐
A repository of pretty cool datasets that I collected for network science and machine learning research.
Mathtoolbox 159 ⭐
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 with Eigen
Danmf 154 ⭐
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
Dpca 133 ⭐
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
Pytorch Spectral Clustering 124 ⭐
[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
Machine_learning_2018 117 ⭐
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
Msmbuilder 108 ⭐
:building_construction: Statistical models for biomolecular dynamics :building_construction:
Walklets 91 ⭐
A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
Swne 83 ⭐
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
Asne 70 ⭐
A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).
B Soid 56 ⭐
Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is an unsupervised learning algorithm written in MATLAB and Python that serves to discover behaviors that are not pre-defined by users.
Kernel Principal Component Analysis Kpca 61 ⭐
KPCA for dimensionality reduction, feature extraction , fault detection, and fault diagnosis
Manifoldlearning.jl 55 ⭐
A Julia package for manifold learning and nonlinear dimensionality reduction
Covid19 Literature Clustering 51 ⭐
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals to find relevant research articles, and respond to rapidly spreading COVID-19 promptly.
Manifold Learning 59 ⭐
Introduction to manifold learning - mathematical theory and applied python examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
Competitive Feature Learning 31 ⭐
Online feature-extraction and classification algorithm that learns representations of input patterns.
Bindash 32 ⭐
Fast and precise comparison of genomes and metagenomes (in the order of terabytes) on a typical personal laptop
Boostedfactorization 25 ⭐
An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).
Unsupervised Learning In R 26 ⭐
Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
Nids Intrusion Detection 26 ⭐
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
Machine_learning_a Z_all_codes_and_templates 23 ⭐
All codes, both created and optimized for best results from the SuperDataScience Course
Keras Temporal Autoencoder 22 ⭐
Keras framework for autocovariance-based dimensionality reduction of time series data with deep neural networks.
Sudhakarkuma Machine_learning 19 ⭐
A repository of resources for understanding the concepts of machine learning/deep learning.
3D Convolutional Autoencoder For Fmri Volumes 17 ⭐
Learning spatial and temporal features of fMRI brain images.
Brainnet Ml Toolbox 17 ⭐
Python Machine Learning Toolbox for Brain Network Classification. Source codes are included of the top 20 teams in the Kaggle competition.
Machine Learning Reference 16 ⭐
This repository contains study materials in the form of presentations (and Python codes) to various Machine Learning techniques and also contains some sample data to practice these algorithms
Hyperspectral_image_analysis_simplified 24 ⭐
Simple hyperspectral image analysis using python and also implements different machine learning techniques.
Enpls 14 ⭐
Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
Crabsort 14 ⭐
🦀🦀🦀 Sort spikes from extra-cellular recordings using neural networks. Fully automated.
Rubixml Har 14 ⭐
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Subspacerobustwasserstein 14 ⭐
Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
Tgplvm 13 ⭐
tGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data
Customer Analytics 12 ⭐
Machine Learning Case study on customer segmentation and prediction of groups.
Stemangiola Nanny 12 ⭐
A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.
Ml Algorithms On Scikit And Keras 10 ⭐
Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..
Dataset Dimensionality Reduction Python 10 ⭐
Here I've demonstrated how and why should we use PCA, KernelPCA, LDA and t-SNE for dimensionality reduction when we work with higher dimensional datasets.
Exploitcnn Rnn 10 ⭐
Exploiting Multi-Layer Features Using a CNN-RNN Approach for RGB-D Object Recognition
Diffusion Map 10 ⭐
Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory
Tensorflow2.0_notebooks 10 ⭐
Implementation of a series of Neural Network architectures in TensorFow 2.0
Dbmap 12 ⭐
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.
Parametricumap_paper 87 ⭐
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).