Embedded Multi-label Feature Selection via Orthogonal Regression

arXiv:2403.00307v1 Announce Type: new
Abstract: In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of features in multi-label classification tasks. Nevertheless, the state-of-the-art embedded multi-label feature selection algorithms based on least square regression usually cannot preserve sufficient discriminative information in multi-label data. To tackle the aforementioned challenge, a novel embedded multi-label feature selection method, termed global redundancy and relevance optimization in orthogonal …

abstract algorithms art arxiv attention classification cs.ai cs.cv embedded feature features feature selection importance least model optimization optimization regression search square state subsets tasks type via