Manifold learning library
For manifold learning, the strongest matches are lmcinnes/umap (This repository provides a dedicated UMAP library for non-linear), krishnaswamylab/phate (This Python library provides PHATE for non-linear dimensionality reduction) and wepe/machinelearning (This repository provides machine learning tools including manifold learning). scikit-learn/scikit-learn and haifengl/smile round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Hand-picked manifold learning libraries for dimensionality reduction, ranked by GitHub stars and activity. Compare and find the best fit.
Manifold learning library
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- lmcinnes/umap
lmcinnes/umap
8,215在 GitHub 上查看This project is a manifold learning and non-linear dimensionality reduction library used to project high-dimensional data into lower-dimensional spaces while preserving topological structure. It functions as a parametric embedding framework and a topological data visualization library for identifying clusters and patterns within complex datasets. The library distinguishes itself through parametric neural mapping, which uses neural networks to learn functional mappings that allow for out-of-sample projections and the reconstruction of original data. It supports supervised and semi-supervised d
This repository provides a dedicated UMAP library for non-linear dimensionality reduction and manifold learning in Python, covering algorithms, visualization tools, and out-of-sample parametric extensions for high-dimensional data.
PythonDimensionality ReductionOut-of-Sample ProjectionsDimensionality Reduction - krishnaswamylab/phate
KrishnaswamyLab/PHATE
This Python library provides PHATE for non-linear dimensionality reduction and manifold learning to help visualize high-dimensional data, though it focuses specifically on its own unique algorithm rather than offering a broad collection of every requested technique.
Dimensionality ReductionDimensionality Reduction - wepe/machinelearning
wepe/MachineLearning
5,714在 GitHub 上查看This project is a machine learning library providing a collection of implementations for supervised and unsupervised learning algorithms. It serves as a deep learning framework, a statistical classifier collection, and a suite of tools for unsupervised learning and dimensionality reduction. The library enables the construction of neural networks, including multi-layer perceptrons and convolutional networks for pattern recognition. It also provides tools for performing principal component analysis and manifold learning to visualize high-dimensional datasets, alongside a suite of clustering alg
This repository provides machine learning tools including manifold learning and dimensionality reduction for high-dimensional data, though it lacks specialized UMAP and t-SNE implementations.
PythonDimensionality Reduction - scikit-learn/scikit-learn
scikit-learn/scikit-learn
66,344在 GitHub 上查看Scikit-learn is a machine learning library for predictive data analysis that provides a collection of algorithms for supervised and unsupervised learning. It functions as a comprehensive toolkit for data preprocessing, dimensionality reduction, and model selection, allowing users to classify data objects, predict continuous values, and cluster similar items based on historical patterns. The project is defined by a unified interface design where objects either learn from data, transform data, or chain these operations into sequential workflows. To ensure performance on large or high-dimensiona
Scikit-learn provides standard machine learning tools including data transformation and dimensionality reduction algorithms like t-SNE and PCA, though it lacks direct UMAP support natively.
PythonDimensionality Reduction Engines - haifengl/smile
haifengl/smile
6,387在 GitHub 上查看Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin
Smile is a JVM machine learning toolkit that includes manifold learning capabilities like multidimensional scaling and geometric manifold embeddings, though it is primarily a broad general-purpose ML library rather than a dedicated dimensionality reduction package and lacks native UMAP or Python-first integration.
JavaDimensionality Reduction Techniques - heucoder/dimensionality_reduction_alo_codes
heucoder/dimensionality_reduction_alo_codes
2,374在 GitHub 上查看Dimensionality reduction alo codes is a Python collection of implementations for classic data reduction algorithms, functioning as a machine learning feature extraction tool. It transforms complex high-dimensional datasets into lower-dimensional spaces through techniques that retain maximum variance, maximize class separability, and translate proximities between data points into spatial distances. The library includes capabilities for eigenvalue variance maximization, linear discriminant projection, nonlinear neighborhood preserving embedding, singular value matrix decomposition, and statist
This repository provides Python implementations of several non-linear dimensionality reduction and manifold learning algorithms, making it a relevant tool for high-dimensional data analysis even though it lacks some of the more advanced features like UMAP or out-of-sample extension.
PythonDimensionality ReductionDimensionality Reduction
