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.
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