Hyper Spectral Image Classification using Machine Learning Methods

It performs principal component analysis (PCA) on a three dimensional normal random vector with covariance matrix specified by the user. It plots data, central ellipsoid, and the projections on the three central planes. For didactic purposes. GNU Octave.

This repository is a comprehensive archive of projects and assignments undertaken for the Pattern Recognition course (TIP8311) at the Federal University of Ceará as part of my Master's curriculum.

Welcome to the "Numerical Methods for Data Analysis" GitHub repository, where I have compiled a collection of insightful projects and analyses conducted during my course on data analysis using MATLAB and typeset with LaTeX.This repository showcases the application of various numerical methods to extract valuable insights from dataset.

Codes des TPs de l'UV de Machine Learning de l'EINA

2023 NCKU Image Processing Homework Code

[IEEE TSP 2021] “Robust Subspace Tracking with Missing Data and Outliers: Novel Algorithm with Convergence Guarantee”. IEEE Transactions on Signal Processing, 2021.

A survey between data reduction techniques for Image Recognition

Simple GUI to load, preview, perform PCA and save spectral data from Hyperspectral images

Research Goal: Determine if there is hemisphere-dependent change in motor signal origin (measured by EEG) in patients who recover motor function through brain-computer interface (BCI) therapy.

Face recognition using various classifiers

This repo leads us to implement the K-means clustering algorithm and apply it to compress an image. And use principal component analysis to find a low-dimensional representation of face images.

Coffee ring effect images analyzed by PCA. Both raw images and feature data extracted from processed images analyzed.

Matlab files for data analytics methods

A facial recognition system in MATLAB that uses the Eigenfaces and PCA techniques to recognize faces.

Machine learning course by Andrew Ng

PCA for face recognition in MATLAB