There are 13 repositories under manifold-learning topic.
:red_circle: 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.
Pytorch implementation of Hyperspherical Variational Auto-Encoders
Manifold-learning flows (ℳ-flows)
Tensorflow implementation of Hyperspherical Variational Auto-Encoders
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
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
A Julia package for manifold learning and nonlinear dimensionality reduction
Tensorflow implementation of adversarial auto-encoder for MNIST
Data Science and Matrix Optimization course
Code for the NeurIPS'19 paper "Guided Similarity Separation for Image Retrieval"
Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
This is the code implementation for the GMML algorithm.
A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
A computational method to rank and infer drug-responsive cell population towards in-silico drug perturbation using a target-perturbed gene regulatory network (tpGRN) for single-cell transcriptomic data
Pytorch code for “Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment ” (DRMEA) (AAAI 2020).
An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.
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.
An interactive 3D web viewer of up to million points on one screen that represent data. Provides interaction for viewing high-dimensional data that has been previously embedded in 3D or 2D. Based on graphosaurus.js and three.js. For a Linux release of a complete embedding+visualization pipeline please visit https://github.com/sonjageorgievska/Embed-Dive.
The software of Pamona, a partial manifold alignment algorithm.
Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.
Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learning
Code for WACV 2022 paper "Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation"
Implemented Laplacian Eigenmaps
This will show how to make autoencoders using pytorch neural networks