dimensionality-reduction
There are 1443 repositories under dimensionality-reduction topic.
lmcinnes/umap
Uniform Manifold Approximation and Projection
seandavi/awesome-single-cell
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
tirthajyoti/Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
benedekrozemberczki/awesome-community-detection
A curated list of community detection research papers with implementations.
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
pavlin-policar/openTSNE
Extensible, parallel implementations of t-SNE
JustGlowing/minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
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
KrishnaswamyLab/PHATE
PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.
maykulkarni/Machine-Learning-Notebooks
Machine Learning notebooks for refreshing concepts.
PAIR-code/umap-js
JavaScript implementation of UMAP
JuliaStats/MultivariateStats.jl
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
jlmelville/uwot
An R package implementing the UMAP dimensionality reduction method.
beringresearch/ivis
Dimensionality reduction in very large datasets using Siamese Networks
GAA-UAM/scikit-fda
Functional Data Analysis Python package
machenslab/dPCA
An implementation of demixed Principal Component Analysis (a supervised linear dimensionality reduction technique)
ardiya/siamesenetwork-tensorflow
Using siamese network to do dimensionality reduction and similar image retrieval
yuki-koyama/mathtoolbox
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C++11 with Eigen
dpeerlab/Palantir
Single cell trajectory detection
iqiukp/KPCA-MATLAB
MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
koba-jon/pytorch_cpp
Deep Learning sample programs using PyTorch in C++
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.
drewwilimitis/Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
wilsonjr/humap
Hierarchical Uniform Manifold Approximation and Projection
PacktWorkshops/The-Data-Science-Workshop
A New, Interactive Approach to Learning Data Science
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).
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:
TorchDR/TorchDR
TorchDR - PyTorch Dimensionality Reduction
TatevKaren/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.