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- W4225158744 abstract "Abstract With the rapid development of information technology, a large amount of unlabeled high-dimensional data has been generated. To be able to better handle these data, we propose a new self-supervised feature selection algorithm for spectral embedding based on block HSIC lasso (FSSBH). It innovatively applies the HSIC theoretical approach to unlabeled scenarios for feature importance assessment, and performs feature selection by self-supervised learning with the pseudo-label matrix formed by the spectral embedding. The algorithm is compared with five popular algorithms for classification experiments on seven publicly available datasets. The results show the superior performance of the FSSBH algorithm." @default.
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- W4225158744 date "2022-04-01" @default.
- W4225158744 modified "2023-09-26" @default.
- W4225158744 title "A self-supervised feature selection algorithm for spectral embedding based on block HSIC lasso" @default.
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- W4225158744 doi "https://doi.org/10.1088/1742-6596/2258/1/012026" @default.
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