LINMA2472: Algorithms in Data Science
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Updated
Mar 18, 2022 - Jupyter Notebook
LINMA2472: Algorithms in Data Science
Kernel PCA and LDA utilizing pytorch
ML course at Warsaw University of Technology
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".
cReddit: Misinformation Assessment Tool for Comments from Reddit
A study of non-linear manifold feature extraction in spike sorting: a comprehensive benchmark of 15 feature extraction methods
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 🤖
My Machine Learning course projects
First Advanced Numerical Methods
Data science Mini projects
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*.
[IEEE TCYB 2021] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
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…
MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
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