Robust Learning Over Multitask Adaptive Networks With Wireless Communication Links

被引:8
作者
Hajiabadi, Mojtaba [1 ]
Hodtani, Ghosheh Abed [1 ]
Khoshbin, Hossein [1 ]
机构
[1] Ferdowsi Univ Mashhad, Dept Elect Engn, Mashhad 9177948974, Razavi Khorasan, Iran
关键词
Adaptive networks; distributed processing; multitask learning; wireless links; correntropy criterion; CORRENTROPY; ADAPTATION;
D O I
10.1109/TCSII.2018.2874090
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Recently, adaptation, optimization and learning over multitask networks with ideal communication links have been investigated in the literature. However, in real-life applications, the communication links between the nodes of the networks are usually non-ideal. In this brief, we consider that the communication links between the nodes of the network are of the wireless type including both block fading and additive noise. In the case of multitask networks with wireless links, non-smart cooperation between the nodes leads to a degraded learning performance that is worse than the noncooperative mode. In this brief, we propose a smart cooperation policy based on an information theoretic criterion, so-called correntropy, that allows the nodes with a similar task and with high quality links to cooperate with each other and rejects the cooperation between the nodes with dissimilar tasks or with low-quality links. The theoretical learning behaviors of the proposed algorithm in the mean sense and in the mean-square sense are also derived. Finally, the computer experiments are provided to verify the theoretical findings.
引用
收藏
页码:1083 / 1087
页数:5
相关论文
共 50 条
  • [31] Multitask diffusion adaptation over hyper-networks
    Bahraini, Tahereh
    Shamsollahi, Mohammad B.
    Afkhaminia, Fatemeh
    DIGITAL SIGNAL PROCESSING, 2025, 158
  • [32] Robust multitask learning in high dimensions under memory constraint
    Chen, Canyi
    Chen, Bingzhen
    Kong, Lingchen
    Zhu, Liping
    STATISTICAL ANALYSIS AND DATA MINING, 2024, 17 (03)
  • [33] Error Saturation Nonlinearities for Robust Incremental LMS over Wireless Sensor Networks
    Panigrahi, Trilochan
    Panda, Ganapati
    Mulgrew, Bernard
    ACM TRANSACTIONS ON SENSOR NETWORKS, 2015, 11 (02)
  • [34] Adaptive processing over distributed networks
    Sayed, Ali H.
    Lopes, G.
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2007, E90A (08) : 1504 - 1510
  • [35] Multicontext multitask learning networks for mass detection in mammogram
    Shen, Rongbo
    Zhou, Ke
    Yan, Kezhou
    Tian, Kuan
    Zhang, Jun
    MEDICAL PHYSICS, 2020, 47 (04) : 1566 - 1578
  • [36] Multitask Representation Learning With Multiview Graph Convolutional Networks
    Huang, Hong
    Song, Yu
    Wu, Yao
    Shi, Jia
    Xie, Xia
    Jin, Hai
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (03) : 983 - 995
  • [37] Multitask Diffusion Adaptation Over Networks With Common Latent Representations
    Chen, Jie
    Richard, Cedric
    Sayed, Ali H.
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2017, 11 (03) : 563 - 579
  • [38] Resilient Multitask Distributed Adaptation Over Networks With Noisy Exchanges
    Wang, Chengcheng
    Tay, Wee Peng
    Wei, Ye
    Wang, Yuan
    2020 IEEE 11TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM), 2020,
  • [39] Incremental adaptive strategies over distributed networks
    Lopes, Cassio G.
    Sayed, Ali H.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2007, 55 (08) : 4064 - 4077
  • [40] On the Effects of Topology and Node Distribution on Learning over Complex Adaptive Networks
    Tu, Sheng-Yuan
    Sayed, Ali H.
    2011 CONFERENCE RECORD OF THE FORTY-FIFTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (ASILOMAR), 2011, : 1166 - 1171