🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.

CellRank: dynamics from multi-view single-cell data

Pytorch implementation of Hyperspherical Variational Auto-Encoders

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

Systematically learn and evaluate manifolds from high-dimensional data

Tensorflow implementation of adversarial auto-encoder for MNIST

TorchDR - PyTorch Dimensionality Reduction

Code for the NeurIPS'19 paper "Guided Similarity Separation for Image Retrieval"

This is the code implementation for the GMML algorithm.

Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

Pytorch code for “Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment ” (DRMEA) (AAAI 2020).

Extended Dynamic Mode Decomposition for system identification from time series data (with dictionary learning, control and streaming options). Diffusion Maps to extract geometric description from data.

Code for WACV 2022 paper "Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation"

TensorFlow Implementation of Manifold Regularized Convolutional Neural Networks.

We propose a density-based estimator for weighted geodesic distances suitable for data lying on a manifold of lower dimension than ambient space and sampled from a possibly nonuniform distribution

Implementation of Low Distortion Local Eigenmaps and several variations of it

ManifoldEM Python suite