文献类型：期刊文献

英文题名：Adaptive weighted dictionary representation using anchor graph for subspace clustering

作者：Feng, Wenyi[1,2,3];Wang, Zhe[1,2];Xiao, Ting[1,2];Yang, Mengping[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Qinghai Univ, Informat Technol Ctr, Xining 810016, Peoples R China

年份：2024

卷号：151

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20241015671357);WOS:【SCI-EXPANDED(收录号:WOS:001200173600001)】；

基金：Acknowledgments This work is supported by Natural Science Foundation of China under Grant No. 62076094, Shanghai Science and Technology Pro-gram, PR China "Federated based cross-domain and cross-task in-cremental learning"under Grant No. 21511100800, Chinese Defense Program of Science and Technology, PR China under Grant No. 2021-JCJQ-JJ-0041, China Aerospace Science and Technology Corporation Industry-University-Research Cooperation Foundation of the Eighth Re-search Institute, PR China under Grant No. SAST2021-007. All authors approved the version of the manuscript to be published.

语种：英文

外文关键词：Dictionary representation; Subspace clustering; Anchor graph; Projection learning

摘要：Samples are commonly represented as sparse vectors in many dictionary representation algorithms. However, this method may result in loss of discriminatory information. Moreover, a redundant dictionary can increase the computational complexity of the algorithm. To tackle these challenges, we propose a novel method named Adaptive Weighted Dictionary Representation using Anchor Graph for Subspace Clustering (AWDR). First, AWDR constructs an anchor graph that encodes the classification information and establishes accurate connectivity components between anchors and clusters, thereby fully utilizing the discriminative information of the original samples. In addition, AWDR learns a complete -dictionary in the subspace to eliminate the noise and out -of -sample effects of the original sample space, while also improving computational efficiency. Finally, AWDR computes the coefficients for the samples in an adaptively weighted manner to find discriminative representation of the samples from the dictionary. Extensive experiments on real -world datasets demonstrate that our method is effective and efficient compared to the state-of-the-art methods.