laplacian-eigenmaps

There are 0 repository under laplacian-eigenmaps topic.

drewwilimitis / Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
python manifold-learning dimensionality-reduction isomap multidimensional-scaling locally-linear-embedding spectral-embedding laplacian-eigenmaps
Language:Jupyter Notebook 206
davisidarta / fastlapmap
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.
dimensionality-reduction embedding laplacian-eigenmaps denoising machine-learning feature-engineering python multithreading scikit-learn
Language:Python 22
JAVI897 / Laplacian-Eigenmaps
Implemented Laplacian Eigenmaps
dimensionality-reduction manifold-learning spectral-embedding laplacian-eigenmaps
Language:Jupyter Notebook 18
jzavatoneveth / laplacian-eigenmaps
Spectral embedding using Laplacian Eigenmaps
dimensionality-reduction laplacian-eigenmaps matlab sparse-matrix
Language:MATLAB 15
nileshkhetrapal / YassQueenDB
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. 📚🧠🚀
database embeddings graph-database laplacian-eigenmaps networkx sentence-encoder tensorflow vector vector-database graph graph-neural-networks machine-learning network-embedding word2vec
Language:Python 14
peterparity / conservation-laws-manifold-learning
Discovering Conservation Laws using Optimal Transport and Manifold Learning
conservation-laws physics chaos diffusion-maps integrable-systems jax laplacian-eigenmaps machine-learning manifold-learning optimal-transport wasserstein-distance
Language:Jupyter Notebook 13
PKU-ML / LaplacianCanonization
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 7
PyDimRed / PyDimRed
A comparison between some dimension reduction algorithms
dimension-reduction pca isomap spectral-embedding random-projection dimensionality-reduction laplacian-eigenmaps
Language:Jupyter Notebook 5
tejasnp163 / Dimensionality-Reduction-on-Wine-Dataset
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
knn-classification pca lda t-sne isomap lle fda wine-dataset kernel-pca pca-analysis laplacian-eigenmaps eda
Language:Jupyter Notebook 4
jasonfilippou / DimReduce
Implementations of 3 linear and non-linear dimensionality reduction algorithms
dimensionality-reduction principal-component-analysis pca isomap cca locality-sensitive-hashing laplacian-eigenmaps manifold-learning manifold-learning-algorithms stiefel-manifold grassman-manifold
Language:Python 3
msarrias / unsupervised-learning-algorithms
Implement some dimensionality reduction and clustering methods and reproduce results from different papers. FLAME clustering, Laplacian Eigenmaps, Spectral clustering...
pca laplacian-eigenmaps kmeans flame
Language:Jupyter Notebook 2
GeorgeMLP / basis-invariance-synthetic-experiment
Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 1
GeorgeMLP / laplacian-canonization
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
gnn laplacian-eigenmaps positional-encoding spectral-embedding
Language:Python 1
jgurakuqi / graph-kernels-and-manifold-svm
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.
graph-kernels isomap laplacian-eigenmaps manifold-learning shortest-path-algorithm sklearn spectral-embedding svm artificial-intelligence machine-learning multiprocessing
Language:Jupyter Notebook 1
arjunsawhney1 / face-ML
In this repo, I demonstrate how simple Linear Algebra concepts can be utilized for powerful image element detection applications
pca image-classification image-recognition linear-algebra eigenvectors laplacian-eigenmaps spectral-embedding
Language:Jupyter Notebook 0

laplacian-eigenmaps

drewwilimitis / Manifold-Learning

davisidarta / fastlapmap

JAVI897 / Laplacian-Eigenmaps

jzavatoneveth / laplacian-eigenmaps

nileshkhetrapal / YassQueenDB

peterparity / conservation-laws-manifold-learning

PKU-ML / LaplacianCanonization

PyDimRed / PyDimRed

tejasnp163 / Dimensionality-Reduction-on-Wine-Dataset

jasonfilippou / DimReduce

msarrias / unsupervised-learning-algorithms

GeorgeMLP / basis-invariance-synthetic-experiment

GeorgeMLP / laplacian-canonization

jgurakuqi / graph-kernels-and-manifold-svm

arjunsawhney1 / face-ML