dimensionality-reduction
There are 1201 repositories under dimensionality-reduction topic.
lmcinnes/umap
Uniform Manifold Approximation and Projection
tirthajyoti/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
seandavi/awesome-single-cell
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
benedekrozemberczki/awesome-community-detection
A curated list of community detection research papers with implementations.
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
JustGlowing/minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
pavlin-policar/openTSNE
Extensible, parallel implementations of t-SNE
benedekrozemberczki/datasets
A repository of pretty cool datasets that I collected for network science and machine learning research.
cvxgrp/pymde
Minimum-distortion embedding with PyTorch
maykulkarni/Machine-Learning-Notebooks
Machine Learning notebooks for refreshing concepts.
KrishnaswamyLab/PHATE
PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.
JuliaStats/MultivariateStats.jl
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
PAIR-code/umap-js
JavaScript implementation of UMAP
beringresearch/ivis
Dimensionality reduction in very large datasets using Siamese Networks
jlmelville/uwot
An R package implementing the UMAP dimensionality reduction method.
GAA-UAM/scikit-fda
Functional Data Analysis Python package
ardiya/siamesenetwork-tensorflow
Using siamese network to do dimensionality reduction and similar image retrieval
machenslab/dPCA
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
yuki-koyama/mathtoolbox
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 with Eigen
koba-jon/pytorch_cpp
Deep Learning sample programs using PyTorch in C++
iqiukp/KPCA-MATLAB
MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
syamkakarla98/Hyperspectral_Image_Analysis_Simplified
The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.
wilsonjr/humap
Hierarchical Uniform Manifold Approximation and Projection
drewwilimitis/Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
danaugrs/go-tsne
t-Distributed Stochastic Neighbor Embedding (t-SNE) in Go
snrazavi/Machine_Learning_2018
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
benedekrozemberczki/DANMF
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
dpeerlab/Palantir
Single cell trajectory detection
PacktWorkshops/The-Data-Science-Workshop
A New, Interactive Approach to Learning Data Science
YttriLab/B-SOID
Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.
dimkastan/PyTorch-Spectral-clustering
[Under development]- Implementation of various methods for dimensionality reduction and spectral clustering implemented with Pytorch
fcomitani/simpsom
Python library for Self-Organizing Maps
jgraving/selfsne
Self-Supervised Noise Embeddings (Self-SNE)
msmbuilder/msmbuilder
:building_construction: Statistical models for biomolecular dynamics :building_construction:
timsainb/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).
dillondaudert/UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation in Julia