MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of clas…

[IEEE TCYB 2021] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

Application of PCA and KPCA algorithms to perform dimensionality reduction on the set of parameters in LPV models

Anomaly detection on a production line using principal component analysis (PCA) and kernel principal component analysis (KPCA) *from scratch*.

My Machine Learning course projects

First Advanced Numerical Methods

Advanced Numerical Methods Project: Face Recognition

Data science Mini projects

Dimensionality reduction

Classification model for predicting whether a patient is at risk of death during hospitalization

KPCA and LDA implementations.

Projects for MSc course: Computational Intelligence and Statistical Learning

A mathematical analysis and implementation of kernel PCA 🤖

LINMA2472: Algorithms in Data Science

This repository contains all program files and datasets used in implementation of Masters Thesis Research Work for the topic - "Efficient Clustering via Kernel Principal Component Analysis and Optimal One Dimensional Clustering".

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