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• W3093153200 abstract "High-dimensional data presents challenges for data management. Feature selection, as an important dimensionality reduction technique, reduces the dimensionality of data by identifying an essential subset of input features, and it can provide interpretable, effective, and efficient insights for analysis and decision-making processes. Algorithmic stability is a key characteristic of an algorithm in its sensitivity to perturbations of input samples. In this paper, first we propose an innovative unsupervised feature selection algorithm. The architecture of our algorithm consists of a feature scorer and a feature selector. The scorer trains a neural network (NN) to score all the features globally, and the selector is in a dependence sub-NN which locally evaluates the representation abilities to select features. Further, we present algorithmic stability analysis and show our algorithm has a performance guarantee by providing a generalization error bound. Empirically, extensive experimental results on ten real-world datasets corroborate the superior generalization performance of our algorithm over contemporary algorithms. Notably, the features selected by our algorithm have comparable performance to the original features; therefore, our algorithm significantly facilitates data management." @default.
• W3093153200 created "2020-10-22" @default.
• W3093153200 creator A5082880020 @default.
• W3093153200 creator A5084134779 @default.
• W3093153200 date "2020-10-19" @default.
• W3093153200 modified "2023-09-27" @default.
• W3093153200 title "A Uniformly Stable Algorithm For Unsupervised Feature Selection." @default.
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• W3093153200 hasPublicationYear "2020" @default.
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