Feature Selection by Eigenvector Centrality

Version (748 KB) by Giorgio
Feature Selection by Eigenvector Centrality for Matlab - Updates 2016


Updated Wed, 21 Dec 2016 08:08:42 +0000

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A selection of recent state of the art "feature ranking and selection" methods for Matlab.

author={G. Roffo and S. Melzi and M. Cristani},
booktitle={2015 IEEE International Conference on Computer Vision (ICCV)},
title={Infinite Feature Selection},
keywords={feature extraction;image classification;image filtering;matrix algebra;object recognition;Inf-FS;classification setting;feature learning strategy;filter-based feature selection;infinite feature selection;matrices;object recognition;Benchmark testing;Convergence;Feature extraction;Joining processes;Object recognition;Redundancy;Standards},

Cite As

Giorgio (2023). Feature Selection by Eigenvector Centrality (https://www.mathworks.com/matlabcentral/fileexchange/54764-feature-selection-by-eigenvector-centrality), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired by: Infinite Feature Selection, Feature Selection Library

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Version Published Release Notes

+ documentation

[1] InfFS
[2] ECFS
[3] mrmr
[4] relieff
[5] mutinffs
[6] fsv
[7] laplacian
[8] mcfs
[9] rfe
[10] L0
[11] fisher
[12] UDFS
[13] llcfs
[14] cfs

- Added new method: Features Selection via Eigenvector Centrality (ECFS) 2016
- Updated the Infinite Feature Selection (InfFS) - Strong improvments on ranking accuracy 2016

Added 9 more feature selection methods from recent literature (2016)


Demo file Added