Papers › Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm

Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm

19 Oct 2020NeurIPS 2021 12arXiv:2010.09416archive 2025-07-28

Xinxing Wu, Qiang Cheng

Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes. Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to globally score all the features, and the selector adopts a dependent sub-NN to locally evaluate the representation abilities for selecting features. Further, we present algorithmic stability analysis and show that our algorithm has a performance guarantee via a generalization error bound. Extensive experimental results on real-world datasets demonstrate superior generalization performance of our proposed algorithm to strong baseline methods. Also, the properties revealed by our theoretical analysis and the stability of our algorithm-selected features are empirically confirmed.

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hierarchy_top_k_keep xinxingwu-uk/ufs/Python/AbsoluteAndOtherAlgorithms/Defined/Functions.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · f42b6bb917023ab6 · report
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Tasks

Decision MakingDimensionality Reductionfeature selection

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Methods

Feature Selection

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