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Infinite Feature Selection

version 4.2 (4.05 KB) by Giorgio
InfFS allows you to rank a huge list of feature, even more than 40000 features and 10000 samples.


Updated 21 Dec 2016

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The Inf-FS is a graph-based method which exploits the convergence properties of the power series of matrices to evaluate the importance of a feature with respect to all the other ones taken together. Indeed, in the Inf-FS formulation, each feature is mapped on an affinity graph, where nodes represent features and weighted edges relationships between them. Each path of a certain length l over the graph is seen as a possible selection of features. Therefore, varying these paths and letting them tend to an infinite number permits the investigation of the importance of each possible subset of features. The Inf-FS assigns a final score to each feature of the initial set; where the score is related to how much the given feature is a good candidate regarding the classification task. Therefore, ranking in descendant order the outcome of the Inf-FS allows us to perform the subset feature selection throughout a model selection stage to determine the number of features to be selected.
Reference : Infinite Feature Selection
Link Paper :

Cite As

Giorgio (2020). Infinite Feature Selection (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (9)

what should be the value of Verbose in code?

PLEASE example for Inf-FS

Why is the calculation of the A is different than the formula propoed in the paper?



This method is included in the FSLib2018. with demo file and examples. please see FSLib at

how to run this code.

Hi, this is an interesting work! Just wondering if you have done similar work using PSO to select the features?

Nicolas Yu

It is amazing~




+ Infinite Feature Selection Dec. 2016: "Unsupervised" & "Supervised" versions.

New methods
[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

- New Inf-FS
- Added 9 feature Selection methods such as: SVM-RFE, Relief-F, mRMR, Laplacian, L0, FSV, Fisher, etc...
- Make file (C/C++ Compiler required)

- some problems fixed

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux