Exploring the hidden dimensions of an optical extreme learning machine

被引:3
|
作者
Silva, Duarte [1 ,2 ]
Ferreira, Tiago [1 ,2 ]
Moreira, Felipe C. [1 ,2 ]
Rosa, Carla C. [1 ,2 ]
Guerreiro, Ariel [1 ,2 ]
Silva, Nuno A. [1 ,2 ]
机构
[1] Univ Porto, Fac Ciencias, Dept Fis & Astron, Rua Campo Alegre S-N, P-4169007 Porto, Portugal
[2] INESC TEC, Ctr Appl Photon, Rua Campo Alegre 687, P-4169007 Porto, Portugal
关键词
Extreme Learning Machine; Optical Computing; Machine Learning; Optics;
D O I
10.1051/jeos/2023001
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Extreme Learning Machines (ELMs) are a versatile Machine Learning (ML) algorithm that features as the main advantage the possibility of a seamless implementation with physical systems. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding in regard to their optical implementations. In this context, this work makes use of an optical complex media and wavefront shaping techniques to implement a versatile optical ELM playground to gain a deeper insight into these machines. In particular, we present experimental evidences on the correlation between the effective dimensionality of the hidden space and its generalization capability, thus bringing the inner workings of optical ELMs under a new light and opening paths toward future technological implementations of similar principles.
引用
收藏
页码:436 / 444
页数:4
相关论文
共 50 条
  • [1] Extreme Learning Machine with initialized hidden weight
    Tavares, L. D.
    Saldanha, R. R.
    Vieira, D. A. G.
    2014 12TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2014, : 43 - +
  • [2] Hidden Node Optimization for Extreme Learning Machine
    Huang, Yan-wei
    Lai, Da-hu
    CONFERENCE ON MODELING, IDENTIFICATION AND CONTROL, 2012, 3 : 375 - 380
  • [3] Negative Correlation Hidden Layer for the Extreme Learning Machine
    Perales-Gonzalez, Carlos
    Fernandez-Navarro, Francisco
    Perez-Rodriguez, Javier
    Carbonero-Ruz, Mariano
    APPLIED SOFT COMPUTING, 2021, 109
  • [4] Constructive hidden nodes selection of extreme learning machine for regression
    Lan, Yuan
    Soh, Yeng Chai
    Huang, Guang-Bin
    NEUROCOMPUTING, 2010, 73 (16-18) : 3191 - 3199
  • [5] Dynamic Adjustment of Hidden Node Parameters for Extreme Learning Machine
    Feng, Guorui
    Lan, Yuan
    Zhang, Xinpeng
    Qian, Zhenxing
    IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (02) : 279 - 288
  • [6] Incremental extreme learning machine with fully complex hidden nodes
    Huang, Guang-Bin
    Li, Ming-Bin
    Chen, Lei
    Siew, Chee-Kheong
    NEUROCOMPUTING, 2008, 71 (4-6) : 576 - 583
  • [7] Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification
    Yang, Yimin
    Wu, Q. M. Jonathan
    IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (12) : 2885 - 2898
  • [8] Exploring QCD matter in extreme conditions with Machine Learning
    Zhou, Kai
    Wang, Lingxiao
    Pang, Long -Gang
    Shi, Shuzhe
    PROGRESS IN PARTICLE AND NUCLEAR PHYSICS, 2024, 135
  • [9] Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning
    Feng, Guorui
    Huang, Guang-Bin
    Lin, Qingping
    Gay, Robert
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2009, 20 (08): : 1352 - 1357
  • [10] Integrated Optimization Method of Hidden Parameters in Incremental Extreme Learning Machine
    Zhang, Siyuan
    Xie, Linbo
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,