Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding

This project includes implementations of the MDS and ISOMAP algorithms using Python and various libraries such as NumPy, Matplotlib, Scikit-learn, and NetworkX.

Open Assessment for Machine Learning and Applications module. This assessment scored 83% and was worth 8 credits of my third year.

The key dimensionality reduction techniques: ISOMAP, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are presented and compared.

My assignments for homework of Computational Data Mining course at Amirkabir University of Technology

Messing around with isometric rendering of tilemaps

A collection of the assignments in the course advanced machine learning

This repository is dedicated to the lab activities of the course of Unsupervised Learning @Units

Dimensionality reduction and data embedding via PCA, MDS, and Isomap.

Isomap is a data visualisation technique based on geodesic distance.

Applied Machine Learning (COMP 551) Course Project

PYTHON PROGRAMMING

Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain

Example implementation of Isomap algorithm in R

Manifold mapping with ISOMAP (MATLAB).

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.

Performing dimensionality reduction with various ML algorithms

Use Manifold Learning, Mapping and Discriminant Analysis to Visualize Image Datasets

Non-linear dimensionality reduction through Isometric Mapping

Project to learn a bit more about dimensionality reduction techniques