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

Implementation for robust ViT and scaled attention

The realization of Kernel Principle Component Analysis (KPCA) and its applications (Code + Description).

Grey Wolf Algorithm Optimization Support Vector Machine Regression Prediction Based on KPCA Kernel Principal Component Analysis (Model Code for Mathematical Modeling Competition)

Illustrate and code both PCA and KPCA.

The project contains code showing various applications of Kernel Principal Components Analysis (KPCA).

Machine learning code with examples in Perceptron, Logic Regression, SVMs, KNNs, PCA, LDA, KPCA, Ensemble Learning, ANNs, CNNs, and DCNN.

This piece of code is used to implement the Kernel PCA (KPCA) algorithm.

Codes for KPCA and PPCA along with the corresponding papers for character and digits dataset.

This is a repository to store code and documentation of experiments made with PCA and kPCA algorithms in python.

This is assignment of this mini project is to code a PCA and KPCA dimensionality reduction functions to project the data distribution we have and separate it using a linear classifier.

This small code applies KPCA on the same wine dataset as I used for PCA, to reduce dimensionality and then applies logistic regression to label new wines to a 1 to 3 group of customer segments.

This is the advanced version of the previously created signature verification code wherein it only used the basic Convolutional Neural Network (CNN) but here, I have used some Image processing techniques such as Gaussian Filters and Difference of Gaussian for noise reduction of the images of the signature and Grey Level Co- occurrence Matrix for feature extraction along with PCA and KPCA for further reducing the dimensions of the dataset. At the end I have used10 different Machine Learning Algorithms for training the datasets.