Year: 2024
Type: article
Abstract: Graph-based semi-supervised feature selection has aroused continuous attention in processing high-dimensional data with most unlabeled and fewer data samples. Many graph-based models perform on a pre-defined graph, which is separated from the procedure of feature selection, making the model hard to select the discriminative features. To address this issue, we exploit a self-adjusted graph for semi-supervised embedded feature selection method (SAGFS), which learns an optimal sparse similarity graph to replace the pre-defined graph to alleviate the effect of data noise. SAGFS allows the learned graph itself to be adjusted according to the local geometric structure of the data and the procedure of selecting features to select the most representative features. Besides that, we introduce $$l_{2,p}$$ l 2 , p -norm to constrain the projection matrix for efficient feature selection. An efficient alternating optimization algorithm is presented, together with analyses on its convergence. Systematical experiments on several publicly datasets are performed to analyze the proposed model from several aspects, and demonstrate that our approaches outperform other comparison methods.
Source: Artificial Intelligence Review
Authors: Jianyong Zhu, Jiaying Zheng, Zhenchen Zhou, Qiong Ding, Feiping Nie
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Awards: G3215844682, G5924243367
OpenAlex ID: W4402994848