# Manifold learning library

> AI-ranked search results for `manifold learning` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 113 total matches; showing the top 6.

Explore on the web: https://awesome-repositories.com/q/manifold-learning

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## Results

- [lmcinnes/umap](https://awesome-repositories.com/repository/lmcinnes-umap.md) (8,215 ⭐) — 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
- [krishnaswamylab/phate](https://awesome-repositories.com/repository/krishnaswamylab-phate.md) (0 ⭐)
- [wepe/machinelearning](https://awesome-repositories.com/repository/wepe-machinelearning.md) (5,714 ⭐) — 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
- [scikit-learn/scikit-learn](https://awesome-repositories.com/repository/scikit-learn-scikit-learn.md) (66,344 ⭐) — 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
- [haifengl/smile](https://awesome-repositories.com/repository/haifengl-smile.md) (6,387 ⭐) — 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
- [heucoder/dimensionality_reduction_alo_codes](https://awesome-repositories.com/repository/heucoder-dimensionality-reduction-alo-codes.md) (2,374 ⭐) — 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