A Julia package for manifold learning and nonlinear dimensionality reduction
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Updated
Mar 2, 2024 - Julia
A Julia package for manifold learning and nonlinear dimensionality reduction
This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.
Lugiato Lefever Equation Solver in Python/Julia
Genetic-algorithm-based optimisation of resonator dispersion for tailoring Kerr comb states of the Lugiato-Lefever Equation (LLE).
A JavaScript Library for Dimensionality Reduction
Applied Machine Learning (COMP 551) Course Project
Implemented Locally Linear Embedding algorithm and some variants of LLE
My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin
Project to learn a bit more about dimensionality reduction techniques
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
Predicting the development of Psoriatic Arthritis using Feature Selection and Locally Linear Embedding
ETH SLT exercise 1 - implement locally linear embedding (LLE)
Comparison of the Stochastic Neighbor Embedding(SNE) and the t-distributed SNE algorithms
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