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.

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

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

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

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

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. 📚🧠🚀

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

Implementations of 3 linear and non-linear dimensionality reduction algorithms

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.

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

A comparison between some dimension reduction algorithms

Discovering Conservation Laws using Optimal Transport and Manifold Learning

Implemented Laplacian Eigenmaps

Spectral embedding using Laplacian Eigenmaps

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