PaCMAP

Dimension reduction method

PaCMAP is an algorithm that reduces the dimensionality of data while preserving both global and local structure

PaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure

GitHub

582 stars
10 watching
56 forks
Language: Jupyter Notebook
last commit: almost 2 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
vyraun/half-sizeAn algorithm to reduce word embeddings to a specified size while maintaining performance129
huanglabpurdue/ncsAn algorithm to reduce noise in images from sCMOS cameras29
wildart/manifoldlearning.jlA package for performing nonlinear dimensionality reduction and manifold learning techniques.92
mljs/pcaTool for reducing dimensionality of data by identifying and preserving the most informative directions.98
epierson9/zifaAn algorithm for dimensionality reduction in single-cell data with applications in genomics and bioinformatics108
xarray-contrib/xeofsTools for dimensionality reduction in climate science data analysis109
tingxueronghua/chartllama-codeA multimodal LLM for understanding and generating charts in various formats.202
zenangst/tailorAn object mapper that simplifies the process of converting data between structured models and unstructured data formats242
clementfarabet/manifoldManages and transforms high-dimensional data into lower-dimensional representations using various algorithms141
hashrock/deno-fnparseA parser combinator library for Deno that provides a simple way to parse CSV data.11
beringresearch/ivisA dimensionality reduction framework using a Siamese Neural Network to visualize high-dimensional datasets332
yuanchao-xu/hyfoA package for data analysis and visualization in hydrology and climate forecasting, providing tools for processing and visualizing NetCDF files.15
ecmwfcode4earth/deeprProject aimed at downscaling global climate data to higher resolutions using deep learning techniques.24
xiaoboxia/cdrAn implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks75
zhengwang100/rsdneA method for creating network embeddings when labeled data is imbalanced12