期刊论文

Ruili Wang

英文

Information Sciences

0020-0255

2023

629

746-759

10.1016/j.ins.2023.01.046

This work is supported by the National Key Research and Development Program of China (No.2021ZD0112400), the Key Program of the National Natural Science Foundation of China (No.62136005), the National Natural Science Foundation of China (No.61976089, No.11201490 and No.72071207), the Natural Science Foundation of Hunan Province under Grant (No.2021JJ20037 and No.2021JJ30451), the Training Program for Excellent Young Innovators of Changsha (No.kq1905031), and the 2020 Catalyst: Strategic NZ-Singapore Data Science Research Programme Fund, MBIE, New Zealand.

本校为其他机构

The “curse of dimensionality” is a bottleneck in big data and artificial intelligence. To reduce the dimensionality of data using the minimal vertex covers of graphs, a discernibility matrix can be applied to construct a hypergraph. However, constructing a hypergraph using a discernibility matrix is a time-consuming and memory-consuming task. To solve this problem, we propose a more efficient approach to graph construction based on a description vector. We develop a graph-based heuristic algorithm for feature selection, named the graph-based ...