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Dimensionality Reduction
101 Open Source Dimensionality Reduction Software Projects
Free and open source dimensionality reduction code projects including engines, APIs, generators, and tools.
Lmcinnes Umap
4195 ⭐
Uniform Manifold Approximation and Projection
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.
Kk7nc Text_classification
1118 ⭐
Text Classification Algorithms: A Survey
Opentsne
699 ⭐
Extensible, parallel implementations of t-SNE
Minisom
650 ⭐
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
Siamesenetwork Tensorflow
245 ⭐
Using siamese network to do dimensionality reduction and similar image retrieval
Uwot
215 ⭐
An R package implementing the UMAP dimensionality reduction method.
Ivis
211 ⭐
Dimensionality reduction in very large datasets using Siamese Networks
Machine Learning Notebooks
197 ⭐
Machine Learning notebooks for refreshing concepts.
Multivariatestats.jl
191 ⭐
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
Umap JS
166 ⭐
JavaScript implementation of UMAP
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).
Go Tsne
142 ⭐
t-Distributed Stochastic Neighbor Embedding (t-SNE) in Go
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:
The Data Science Workshop
105 ⭐
A New, Interactive Approach to Learning Data Science
Twpca
96 ⭐
🕝 Time-warped principal components analysis (twPCA)
Tkonopka Umap
92 ⭐
Uniform Manifold Approximation and Projection - R package
Walklets
91 ⭐
A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).
Enstop
87 ⭐
Ensemble topic modelling with pLSA
Tsne Viz
81 ⭐
Python Wrapper for t-SNE Visualization
Simpsom
83 ⭐
Python library for Self-Organizing Maps
Swne
83 ⭐
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
Lfda
71 ⭐
Local Fisher Discriminant Analysis in R
Bane
73 ⭐
A sparsity aware implementation of "Binarized Attributed Network Embedding" (ICDM 2018).
Asne
70 ⭐
A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).
Sine
66 ⭐
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).
Umap.jl
65 ⭐
Uniform Manifold Approximation and Projection (UMAP) implementation in Julia
Dimred
62 ⭐
A Framework for Dimensionality Reduction in R
Dpeerlab Palantir
62 ⭐
Single cell trajectory detection
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
Dml
55 ⭐
R package for Distance Metric Learning
Manifoldlearning.jl
55 ⭐
A Julia package for manifold learning and nonlinear dimensionality reduction
Pymorton
53 ⭐
A lightweight and efficient Python Morton encoder with support for geo-hashing
Smallvis
49 ⭐
R package for dimensionality reduction of small datasets
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.
Spotifav
50 ⭐
🤘 Map out your musical taste on Spotify with machine learning
Manifold Learning
59 ⭐
Introduction to manifold learning - mathematical theory and applied python examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
Schpf
42 ⭐
Single-cell Hierarchical Poisson Factorization
Ml2017
36 ⭐
2017 Spring (105-2) -- Machine Learning
Vlgp
37 ⭐
Variational Latent Gaussian Process
Spca
35 ⭐
Sparse Principal Component Analysis (SPCA) using Variable Projection
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
Windqaq Ml2017
30 ⭐
NTUEE Machine Learning, 2017 Spring
Theislab Destiny
32 ⭐
R package for single cell and other data analysis using diffusion maps
People Map
27 ⭐
Visualization Tool for Mapping Out Researchers using Natural Language Processing
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).
Sef
25 ⭐
A Python Library for Similarity-based Dimensionality Reduction
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.
Ncvis
20 ⭐
Noise-Contrastive Visualization
Tsnet
19 ⭐
Source code accompanying the submission to EuroVis 2017
Sudhakarkuma Machine_learning
19 ⭐
A repository of resources for understanding the concepts of machine learning/deep learning.
Drcomparison
20 ⭐
Comparison of dimensionality reduction methods
Online_psp_matlab
18 ⭐
Benchmark of online PCA algorithms
Spdnet
18 ⭐
Implementation of Deep SPDNet in pytorch
Timecorr
18 ⭐
Estimate dynamic high-order correlations in multivariate timeseries data
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
Tsne Animation
17 ⭐
Hacking sklearn's t-SNE implementation to animate embedding process
Hyperspectral_image_analysis_simplified
24 ⭐
Simple hyperspectral image analysis using python and also implements different machine learning techniques.
Moses
16 ⭐
Streaming, Memory-Limited, r-truncated SVD Revisited!
Adenine
15 ⭐
ADENINE: A Data ExploratioN PipelINE
Dynamicalcomponentsanalysis
15 ⭐
Dynamical Components Analysis
Singlecellworkflow
13 ⭐
Tutorial for the analysis of scRNA-seq data in R
Enpls
14 ⭐
Algorithmic framework for measuring feature importance, outlier detection, model applicability evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
Uapca
14 ⭐
Uncertainty-aware principal component analysis.
Uscbiostats Partition
14 ⭐
A fast and flexible framework for data reduction in R
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.
Pcaworkshop
14 ⭐
An introduction to matrix factorization and PCA and SVD.
Opc
13 ⭐
Optimal Projections for Clustering
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
Signal Processing
14 ⭐
Repository that gathers code for signal processing
Sneer
12 ⭐
Stochastic Neighbor Embedding Experiments in R
Roleo
12 ⭐
Web based semantic visualization tool
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.
Public_kssd
11 ⭐
K-mer substring space decomposition
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
Trimap Pytorch
13 ⭐
Implementation of TriMap dimensionality reduction in PyTorch
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).
Pytorch_cpp
60 ⭐
Deep Learning sample programs using PyTorch in C++