@inproceedings{8aade86fb5ef4adeb5f95b4af6cb8b5a,
title = "Robust Adaptive Sparse Learning Method for Graph Clustering",
abstract = "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 ell-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.",
keywords = "Clustering, Graph Construction, Manifold Structure, Sparse Learning",
author = "Mulin Chen and Qi Wang and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451374",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1618--1622",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
}