ROBUST ADAPTIVE SPARSE LEARNING METHOD FOR GRAPH CLUSTERING

被引：0
|
作者：
Chen, Mulin [1 ,2 ]
Wang, Qi [1 ,2 ,3 ]
Li, Xuelong [4 ,5 ]
机构：
[1] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
[2] Northwestern Polytech Univ, Ctr OpT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
[3] Northwestern Polytech Univ, USRI, Xian 710072, Shaanxi, Peoples R China
[4] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
[5] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
来源：
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2018年
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Clustering; Manifold Structure; Graph Construction; Sparse Learning;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Graph clustering aims to group the data into clusters according to a similarity graph, and has received sufficient attention in computer vision. As the basis of clustering, the quality of graph affects the results directly. In this paper, a Robust Adaptive Sparse Learning (RASL) method is proposed to improve the graph quality. The contributions made in this paper are three fold: (1) the sparse representation technique is employed to enforce the graph sparsity, and the l(2,1) norm is introduced to improve the robustness; (2) the intrinsic manifold structure is captured by investigating the local relationship of data points; (3) an efficient optimization algorithm is designed to solve the proposed problem. Experimental results on various real-world benchmark datasets demonstrate the promising results of the proposed graph-based clustering method.
引用
收藏
页码：1618 / 1622
页数：5
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