Feature Selection Experimental Code
Published:
Feature Selection Research Implementation
A comprehensive collection of unsupervised feature selection algorithms and experimental frameworks developed for academic research purposes.
Project Overview
This repository contains implementations and experiments related to unsupervised feature selection methods, directly supporting multiple research publications and ongoing academic work.
Key Components
- Multiple unsupervised feature selection algorithms
- Experimental frameworks for algorithm comparison
- Performance evaluation metrics
- Data preprocessing utilities
- Research reproducibility tools
Technologies Used
- Programming Language: MATLAB
- Development Period: August 2025
- Application Area: Machine Learning Research
Research Impact
The algorithms and experiments in this project have directly contributed to multiple academic publications in the field of feature selection and information theory. The code serves as a foundation for ongoing research in maximizing joint entropy and pattern discrimination.
Academic Integration
This work is closely tied to the Master’s thesis research on “A Study on Maximizing Joint Entropy and Pattern Discrimination for Unsupervised Feature Selection” and has been referenced in multiple conference and journal papers.