In manipulating high-dimensional data arising from real applications, dimension reduction methods have been proven to be effective and necessary. Nevertheless, the widespread selection bias, induced in the process of data collection, has long been unaware and its negative effects have been ignored. The acciden-tal spurious correlation between the features brought by selection bias will deteriorate the performance of dimension reduction approaches. It is vital to remove such bias and consider its influence on dimen-sion reduction. In this paper, we propose a novel decorrelated spectral regression algorithm for unsuper-vised dimension reduction on data with sample selection bias. In this method, the sample weights of the global feature distribution are learned by introducing the decorrelation regularizer. The learned weights are then combined with the regression model to obtain the dimension reduction mapping. Thus, the dimension reduction results are less affected by the accidental spurious correlation of data caused by selection bias. In addition, we also propose the updating rules of the parameters in our algorithm. A large number of experimental results on real-world datasets show that our method has achieved a significant improvement, indicating that it is effective and necessary to remove the accidental spurious correlation between features caused by selection bias in dimension reduction. & COPY; 2023 Elsevier B.V. All rights reserved.
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