Adaptive graph nonnegative matrix factorization with the self-paced regularization

被引：0
作者：
Xuanhao Yang
Hangjun Che
Man-Fai Leung
Cheng Liu
机构：
[1] Southwest University,College of Electronic and Information Engineering
[2] Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing,School of Computing and Information Science, Faculty of Science and Engineering
[3] Anglia Ruskin University,Department of Computer Science
[4] Shantou University,undefined
来源：
Applied Intelligence | 2023年 / 53卷
关键词：
Clustering; Nonnegative matrix factorization; Self-paced learning; Adaptive neighbors;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Nonnegative matrix factorization (NMF) is a popular approach to extract intrinsic features from the original data. As the nonconvexity of NMF formulation, it always leads to degrade the performance. To alleviate the defect, in this paper, the self-paced regularization is introduced to find a better factorized matrices by sequentially selecteing data in the learning process. Additionally, to find the low-dimensional manifold embeded in the high-dimensional space, adaptive graph is introduced by using dynamic neighbors assignment. An alternating iterative algorithm is designed to sovle the proposed mathematical factorization formulation. The experimental results are given to show the effectiveness of the proposed approach in comparison with state-of-the-art algorithms on six public datasets.
引用
收藏
页码：15818 / 15835
页数：17
相关论文
共 42 条
[1]  
Park TJ(2020)Auto-tuning spectral clustering for speaker diarization using normalized maximum eigengap IEEE Sig Process Lett 27 381-385
[2]  
Han KJ(2019)Enhancing hyperspectral unmixing with two-stage multiplicative update nonnegative matrix factorization IEEE Access 7 171023-171031
[3]  
Kumar M(2022)Fast and secure distributed nonnegative matrix factorization IEEE Trans Knowl Data Eng 34 653-666
[4]  
Narayanan S(1996)Visual object recognition Annu Rev Neurosci 19 577-621
[5]  
Sun L(1994)Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque Cereb Cortex 4 509-522
[6]  
Zhao K(2021)Graph regularized nonnegative matrix factorization with label discrimination for data clustering Neurocomputing 440 297-309
[7]  
Han C(2019)Self-paced and soft-weighted nonnegative matrix factorization for data representation Knowl-Based Syst 164 29-37
[8]  
Liu Z(2020)Regularized nonnegative matrix factorization with adaptive local structure learning Neurocomputing 382 196-209
[9]  
Qian Y(2019)Self-paced multi-task clustering Neurocomputing 350 212-220
[10]  
Tan C(2010)Convex and semi-nonnegative matrix factorizations IEEE Trans Pattern Anal Mach Intell 32 45-55
12345