GitHub topics: kpca

Repositories

iqiukp/KPCA-MATLAB

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

Language: MATLAB - Size: 6.65 MB - Last synced at: 1 day ago - Pushed at: over 3 years ago - Stars: 250 - Forks: 80

sylvaincom/anomaly-detection-PCA

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

Language: MATLAB - Size: 431 KB - Last synced at: about 2 months ago - Pushed at: over 4 years ago - Stars: 8 - Forks: 1

Nachiket-Bhide/Masters-Thesis-Research-Work

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".

Language: Python - Size: 330 KB - Last synced at: 11 months ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

aditijoshi613/Patient-Survival-Prediction

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

Language: Jupyter Notebook - Size: 104 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

saeedghoorchian/Dimensionality-Reduction-Using-KPCA

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

Language: Matlab - Size: 2.54 MB - Last synced at: about 1 year ago - Pushed at: about 7 years ago - Stars: 6 - Forks: 5

ChenHongruixuan/KPCAMNet

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

Language: Python - Size: 4.17 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 54 - Forks: 46

Xiaoyang-Rebecca/PatternRecognition_Matlab

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 classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

Language: MATLAB - Size: 11.7 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 65 - Forks: 21