laplacian-eigenmaps

Here are 15 public repositories matching this topic...

PKU-ML / LaplacianCanonization

Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

spectral-embedding laplacian-eigenmaps gnn positional-encoding

Updated Jan 20, 2024
Python

GeorgeMLP / laplacian-canonization

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Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

spectral-embedding laplacian-eigenmaps gnn positional-encoding

Updated Jan 20, 2024
Python

GeorgeMLP / basis-invariance-synthetic-experiment

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Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

spectral-embedding laplacian-eigenmaps gnn positional-encoding

Updated Oct 26, 2023
Python

peterparity / conservation-laws-manifold-learning

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Discovering Conservation Laws using Optimal Transport and Manifold Learning

machine-learning physics chaos conservation-laws diffusion-maps manifold-learning optimal-transport jax laplacian-eigenmaps integrable-systems wasserstein-distance

Updated Sep 23, 2023
Jupyter Notebook

jgurakuqi / graph-kernels-and-manifold-svm

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This project aims to compare the performance obtained using a linear Support Vector Machine model whose data was first processed through a Shortest Path kernel with the same SVM, this time with data also processed by two alternative Manifold Learning techniques: Isomap and Spectral Embedding.

machine-learning multiprocessing svm sklearn artificial-intelligence manifold-learning graph-kernels isomap shortest-path-algorithm spectral-embedding laplacian-eigenmaps

Updated Aug 18, 2023
Jupyter Notebook

nileshkhetrapal / YassQueenDB

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Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀

machine-learning database graph tensorflow word2vec vector embeddings networkx graph-database network-embedding laplacian-eigenmaps graph-neural-networks sentence-encoder vector-database

Updated May 26, 2023
Python

davisidarta / fastlapmap

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Fast Laplacian Eigenmaps: lightweight multicore LE for non-linear dimensional reduction with minimal memory usage. Outperforms sklearn's implementation and escalates linearly beyond 10e6 samples.

python machine-learning scikit-learn multithreading dimensionality-reduction feature-engineering embedding denoising laplacian-eigenmaps

Updated Nov 12, 2021
Python

JAVI897 / Laplacian-Eigenmaps

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Implemented Laplacian Eigenmaps

dimensionality-reduction manifold-learning spectral-embedding laplacian-eigenmaps

Updated Oct 7, 2021
Jupyter Notebook

arjunsawhney1 / face-ML

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In this repo, I demonstrate how simple Linear Algebra concepts can be utilized for powerful image element detection applications

linear-algebra pca image-classification image-recognition eigenvectors spectral-embedding laplacian-eigenmaps

Updated Sep 30, 2021
Jupyter Notebook

msarrias / unsupervised-learning-algorithms

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Implement some dimensionality reduction and clustering methods and reproduce results from different papers. FLAME clustering, Laplacian Eigenmaps, Spectral clustering...

pca kmeans flame laplacian-eigenmaps

Updated Feb 10, 2021
Jupyter Notebook

jasonfilippou / DimReduce

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Implementations of 3 linear and non-linear dimensionality reduction algorithms

pca dimensionality-reduction cca locality-sensitive-hashing principal-component-analysis manifold-learning isomap laplacian-eigenmaps stiefel-manifold manifold-learning-algorithms grassman-manifold

Updated Jan 12, 2021
Python

tejasnp163 / Dimensionality-Reduction-on-Wine-Dataset

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Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.

fda eda pca-analysis pca t-sne lda lle isomap knn-classification kernel-pca laplacian-eigenmaps wine-dataset

Updated Nov 2, 2020
Jupyter Notebook

drewwilimitis / Manifold-Learning

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Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

python dimensionality-reduction manifold-learning isomap multidimensional-scaling spectral-embedding laplacian-eigenmaps locally-linear-embedding