.png){kind=icon}
返回
An outlier-robust smoothness-based graph learning approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2023.108927.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph learning (GL) is a tool for finding direct relationships between the nodes of a network, and hence, inferring the graph topology from the data. Recently, many GL algorithms have been proposed in the field of graph signal processing, which are based on smoothness of the graph signals on the learned graph. However, although it is possible for the input graph signals to be contaminated by outliers, for example due to sensor failures or temporary faulty information records, existing techniques are very vul-nerable to outliers. So, the goal is to infer a graph topology to be, as much as possible, insensitive to this kind of data corruptions. To this aim, due to the sparse nature of outlier data, we propose a new approach for robustifying GL algorithms by incorporating L1-norm or squared L1-norm terms into the objective function of smoothness based GL methods, yielding to a non-convex minimization problem. A novel iterative minimization method is introduced to solve the resulting non-convex problem. Moreover, the convergence of the algorithm is established despite of its non-convex nature. In simulations, the high performance of the proposed algorithm is demonstrated in presence of a considerably large amount of outliers.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Graph signal processing
Graph learning
Outlier compensation
Block coordinate descent
Convergence analysis
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
Compression and Air Storage Systems for Small Size CAES Plants: Design and Off-design Analysis小型CAES工厂的压缩和空气存储系统: 设计和非设计分析
OAAI
OAAI分享收藏K-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph LearningK-graps: 一种图信号聚类与多图学习算法
PREAI
分享收藏分享收藏分享收藏Graph Signal Processing: Overview, Challenges, and Applications图信号处理: 概述、挑战与应用.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏Energy-efficient load balancing scheme for two-tier communication in wireless sensor networks无线传感器网络中节能的两层通信负载均衡方案
PREAI
分享收藏分享收藏
查看更多内容