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基于极限学习机参数迁移的域适应算法

许夙晖 慕晓冬 柴栋 罗畅

许夙晖, 慕晓冬, 柴栋, 罗畅. 基于极限学习机参数迁移的域适应算法. 自动化学报, 2018, 44(2): 311-317. doi: 10.16383/j.aas.2018.c160818
引用本文: 许夙晖, 慕晓冬, 柴栋, 罗畅. 基于极限学习机参数迁移的域适应算法. 自动化学报, 2018, 44(2): 311-317. doi: 10.16383/j.aas.2018.c160818
XU Su-Hui, MU Xiao-Dong, CHAI Dong, LUO Chang. Domain Adaption Algorithm with ELM Parameter Transfer. ACTA AUTOMATICA SINICA, 2018, 44(2): 311-317. doi: 10.16383/j.aas.2018.c160818
Citation: XU Su-Hui, MU Xiao-Dong, CHAI Dong, LUO Chang. Domain Adaption Algorithm with ELM Parameter Transfer. ACTA AUTOMATICA SINICA, 2018, 44(2): 311-317. doi: 10.16383/j.aas.2018.c160818

基于极限学习机参数迁移的域适应算法

doi: 10.16383/j.aas.2018.c160818
详细信息
    作者简介:

    慕晓冬  火箭军工程大学信息工程系教授.主要研究方向为智能信息处理与计算机仿真.E-mail:muxiaodong402@163.com

    柴栋  北京航空工程技术研究中心工程师, 博士.主要研究方向为智能信息处理.E-mail:chaibaodong@126.com

    罗畅  空军工程大学防空反导学院博士研究生.主要研究方向为智能信息处理.E-mail:luochang1988@126.com

Domain Adaption Algorithm with ELM Parameter Transfer

More Information
    Author Bio:

     Professor in the Department of Information Engineering, Rocket Force University of Engineering. His research interest covers intelligent information processing and computer simulation

     Ph. D, Engineer at Beijing Aeronautical Technology Research Institute. His main research interest covers intelligent information processing

     Ph. D. candidate at the Air and Missile Defense College, Air Force Engineering University. His main research interest is intelligent information processing

    Corresponding author: XU Su-Hui  Ph. D. candidate in the Department of Information Engineering, Rocket Force University of Engineering. Her main research interest is remote sensing image processing. Corresponding author of this paper
  • 摘要: 针对含少量标签样本的迁移学习问题,本文提出了基于极限学习机(Extreme learning machine,ELM)参数迁移的域适应算法,其核心思想是将目标域的ELM分类器参数投影到源域参数空间中,使其最大限度地与源域的分类器参数分布相同.此外,考虑到迁移中有可能带来负迁移的情况,在目标函数中引入正则项约束.本文算法与以往的域适应算法相比优势在于,其分类器参数以及转移矩阵是同时优化得到的,并且其目标函数求解过程相对简单.实验结果表明,与主流的域适应算法相比,本文算法在精度与效率上都表现出明显的优势.
    1)  本文责任编委 王占山
  • 图  1  极限学习机网络结构

    Fig.  1  ELM network

    图  2  bing-caltech数据集分类精度

    Fig.  2  The accuracy on bing-caltech dataset

    图  3  分类精度随$L$变化曲线

    Fig.  3  The accuracy curves varying with $L$

    图  4  $C_1$与$C_2$不同取值下的精度曲线

    Fig.  4  The accuracy curves varying with $C_1$ and $C_2$

    表  1  本文方法与主流算法适用范围对比

    Table  1  The application comparison between DAPT and previous methods

    ARC-t GFK HFA MMDT DAPT
    多类分类
    大规模数据集
    解决异构特征
    下载: 导出CSV

    表  2  使用BOVW特征时算法的分类精度(%)

    Table  2  Accuracy for all the methods when using BOVW feature (%)

    SVMs SVMt ELMs ELMt ARC-t GFK HFA MMDT DAPT_$n$ DAPT
    a$\rightarrow$w 30.30 50.02 40.21 69.42 56.77 57.10 55.71 64.85 63.60 70.25
    a$\rightarrow$d 32.20 47.28 38.66 56.42 54.37 54.14 50.18 54.37 50.75 58.19
    a$\rightarrow$c 39.55 26.50 42.23 35.99 31.66 37.95 37.03 39.73 31.96 44.29
    w$\rightarrow$a 28.42 39.28 38.42 52.73 43.28 41.25 43.44 50.47 48.79 54.82
    w$\rightarrow$d 63.74 45.63 70.16 55.28 55.91 75.51 71.29 62.72 52.76 71.93
    w$\rightarrow$c 24.55 26.15 33.31 33.74 30.50 31.59 31.92 34.81 30.45 38.66
    d$\rightarrow$a 31.54 39.29 38.49 51.16 42.76 40.52 42.45 50.40 47.52 53.98
    d$\rightarrow$w 66.06 47.45 80.74 67.96 59.64 80.27 78.33 74.30 62.68 81.92
    d$\rightarrow$c 27.72 26.28 33.80 36.52 31.29 33.97 33.52 35.83 31.43 40.56
    c$\rightarrow$a 37.18 38.57 44.76 52.06 44.88 38.38 44.11 51.00 48.83 55.98
    c$\rightarrow$w 26.83 48.49 36.34 67.68 55.89 52.24 55.90 62.81 62.25 69.17
    c$\rightarrow$d 34.53 45.98 40.63 57.09 54.76 57.45 50.64 52.68 49.09 59.17
    acc (%) 36.88 40.08 44.81 53.00 55.77 50.03 49.54 52.83 48.34 58.24
    time (s) 0.059 0.009 0.17 0.15 4.0245 0.264 3.26 0.485 0.18 0.19
    下载: 导出CSV

    表  3  使用深度特征时算法的分类精度(%)

    Table  3  Accuracy for all the methods when using deep feature (%)

    SVMs SVMt ELMs ELMt ARC-t GFK HFA MMDT DAPT_$n$ DAPT
    a$\rightarrow$w 72.81 82.00 77.53 89.92 91.04 92.41 90.23 90.92 92.47 92.83
    a$\rightarrow$d 75.12 84.29 82.17 92.60 93.31 93.76 94.19 93.58 94.76 95.00
    a$\rightarrow$c 76.00 67.35 80.01 76.03 76.83 81.13 80.05 81.38 78.59 82.14
    w$\rightarrow$a 72.54 79.82 79.46 86.97 87.80 89.62 87.91 88.85 89.83 90.90
    w$\rightarrow$d 92.24 83.74 95.43 90.94 92.68 97.96 96.25 95.24 94.45 98.46
    w$\rightarrow$c 66.94 65.32 70.86 75.95 77.70 79.12 75.40 76.47 78.28 81.50
    d$\rightarrow$a 79.26 78.39 83.64 86.74 86.96 89.07 87.21 87.33 90.09 91.70
    d$\rightarrow$w 92.11 80.53 96.32 89.15 90.42 98.02 96.65 96.00 92.21 98.34
    d$\rightarrow$c 73.41 67.08 76.51 76.03 77.96 79.77 76.67 75.54 78.83 82.41
    c$\rightarrow$a 80.29 79.57 86.50 86.16 87.13 88.94 87.92 89.14 89.25 90.91
    c$\rightarrow$w 70.66 82.66 74.17 89.38 90.36 90.71 89.33 89.64 91.79 92.34
    c$\rightarrow$d 76.93 84.33 81.50 90.51 91.77 90.80 90.78 91.02 93.11 93.82
    acc (%) 77.36 77.92 82.01 85.87 87.00 89.27 87.72 87.93 88.64 90.86
    time (s) 0.204 0.039 0.61 0.55 6.67 13.55 9.26 2.84 1.66 1.74
    下载: 导出CSV
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  • 收稿日期:  2016-12-11
  • 录用日期:  2017-03-30
  • 刊出日期:  2018-02-20

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