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.
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
Passenger Flow Optimisation in the Singapore MRT network through Linear Algebra, Monte Carlo Simulation, Dijkstra's Algorithm, Graph Theory and Pareto Frontier Optimisation
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
To associate your repository with the laplacian-eigenmaps topic, visit your repo's landing page and select "manage topics."