Zhao, Zheng; Wang, Lei; Liu, Huan
Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant...[Show more]
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.