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基于低秩表示的魯棒判別特征子空間學(xué)習(xí)模型

李驁 劉鑫 陳德運(yùn) 張英濤 孫廣路

李驁, 劉鑫, 陳德運(yùn), 張英濤, 孫廣路. 基于低秩表示的魯棒判別特征子空間學(xué)習(xí)模型[J]. 電子與信息學(xué)報(bào), 2020, 42(5): 1223-1230. doi: 10.11999/JEIT190164
引用本文: 李驁, 劉鑫, 陳德運(yùn), 張英濤, 孫廣路. 基于低秩表示的魯棒判別特征子空間學(xué)習(xí)模型[J]. 電子與信息學(xué)報(bào), 2020, 42(5): 1223-1230. doi: 10.11999/JEIT190164
Ao LI, Xin LIU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN. Robust Discriminative Feature Subspace Learning Based on Low Rank Representation[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1223-1230. doi: 10.11999/JEIT190164
Citation: Ao LI, Xin LIU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN. Robust Discriminative Feature Subspace Learning Based on Low Rank Representation[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1223-1230. doi: 10.11999/JEIT190164

基于低秩表示的魯棒判別特征子空間學(xué)習(xí)模型

doi: 10.11999/JEIT190164
基金項(xiàng)目: 國家自然科學(xué)基金 (61501147),黑龍江省青年創(chuàng)新人才計(jì)劃(UNPYSCT-2018203),黑龍江省自然科學(xué)基金優(yōu)秀青年基金(YQ2019F011),黑龍江省高等學(xué)?;究蒲袠I(yè)務(wù)專項(xiàng) (LGYC2018JQ013),哈爾濱市應(yīng)用技術(shù)研究與開發(fā)項(xiàng)目(2017RALX006)
詳細(xì)信息
    作者簡(jiǎn)介:

    李驁:男,1986年生,博士,副教授,研究方向?yàn)橛?jì)算機(jī)視覺及其模式識(shí)別、機(jī)器學(xué)習(xí)

    劉鑫:男,1993年生,碩士生,研究方向?yàn)闄C(jī)器學(xué)習(xí)、模式識(shí)別

    陳德運(yùn):男,1962年生,博士,教授,博士生導(dǎo)師,研究方向?yàn)樘綔y(cè)與成像技術(shù)、模式識(shí)別

    張英濤:女,1975年生,博士,副教授,研究方向?yàn)槿斯ぶ悄芘c信息處理

    孫廣路:男,1979年生,博士,教授,博士生導(dǎo)師,研究方向?yàn)闄C(jī)器學(xué)習(xí)、網(wǎng)絡(luò)安全

    通訊作者:

    李驁 dargonboy@126.com

  • 中圖分類號(hào): TN911.73

Robust Discriminative Feature Subspace Learning Based on Low Rank Representation

Funds: The National Natural Science Foundation of China(61501147), The University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2018203), The Natural Science Foundation of Heilongjiang Province(YQ2019F011), The Fundamental Research Foundation for University of Heilongjiang Province (LGYC2018JQ013), The Application Research and Development Project of Harbin(2017RALX006)
  • 摘要:

    特征子空間學(xué)習(xí)是圖像識(shí)別及分類任務(wù)的關(guān)鍵技術(shù)之一,傳統(tǒng)的特征子空間學(xué)習(xí)模型面臨兩個(gè)主要的問題。一方面是如何使樣本在投影到特征空間后有效地保持其局部結(jié)構(gòu)和判別性。另一方面是當(dāng)樣本含噪時(shí)傳統(tǒng)學(xué)習(xí)模型所發(fā)生的失效問題。針對(duì)上述兩個(gè)問題,該文提出一種基于低秩表示(LRR)的判別特征子空間學(xué)習(xí)模型,該模型的主要貢獻(xiàn)包括:通過低秩表示探究樣本的局部結(jié)構(gòu),并利用表示系數(shù)作為樣本在投影空間的相似性約束,使投影子空間能夠更好地保持樣本的局部近鄰關(guān)系;為提高模型的抗噪能力,構(gòu)造了一種利用低秩重構(gòu)樣本的判別特征學(xué)習(xí)約束項(xiàng),同時(shí)增強(qiáng)模型的判別性和魯棒性;設(shè)計(jì)了一種基于交替優(yōu)化技術(shù)的迭代數(shù)值求解方案來保證算法的收斂性。該文在多個(gè)視覺數(shù)據(jù)集上進(jìn)行分類任務(wù)的對(duì)比實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明所提算法在分類準(zhǔn)確度和魯棒性方面均優(yōu)于傳統(tǒng)特征學(xué)習(xí)方法。

  • 圖  1  基于樣本局部近鄰關(guān)系的特征空間投影效果示意圖

    圖  2  低秩表示約束的魯棒特征學(xué)習(xí)模型的效果示意圖

    圖  3  不同比例的隨機(jī)脈沖噪聲下的識(shí)別率曲線

    圖  4  不同比例的隨機(jī)條紋干擾下的識(shí)別率曲線

    圖  5  不同訓(xùn)練樣本數(shù)量下的識(shí)別率曲線

    圖  6  參數(shù)取值與分類準(zhǔn)確率的變化關(guān)系曲線

    圖  7  目標(biāo)函數(shù)值隨迭代次數(shù)的收斂曲線

    算法1:綜合目標(biāo)函數(shù)的數(shù)值求解方案
     輸入: 訓(xùn)練集X,類別標(biāo)簽Y, ${\lambda _1}$, ${\lambda _2}$, $\eta $, ${{Z}} = {{G}} = {{R}} = 0$,
     ${{E}} = 0$, ${{{Y}}_{\rm{1}}} = {{{Y}}_{\rm{2}}} = {{{Y}}_{\rm{3}}} = 0$, $\mu = 0.6$, ${\mu _{\max }} = {10^{10}}$, $\rho = 1.1$。
     輸出: ${{P}}$
     While not convergence do
     1. 使用式(5)—(9)進(jìn)行更新${{{P}}^{k + 1}}$, ${{{G}}^{k + 1}}$, ${{{R}}^{k + 1}}$, ${{{Z}}^{k + 1}}$, ${{{E}}^{k + 1}}$;
     2. 更新拉格朗日乘子及參數(shù)$\mu $:
      ${{{Y}}_1}^{k + 1} = {{{Y}}_1}^k + \mu \left( {{{X}} - {{X}}{{{Z}}^{k + 1}} - {{{E}}^{k + 1}}} \right)$;
      ${{{Y}}_2}^{k + 1} = {{{Y}}_2}^k + \mu \left( {{{{Z}}^{k + 1}} - {{{G}}^{k + 1}}} \right)$;
      ${{{Y}}_3}^{k + 1} = {{{Y}}_3}^k + \mu \left( {{{{Z}}^{k + 1}} - {{{R}}^{k + 1}}} \right)$;
      $\mu = \min \left( {{\mu _{\max }},\rho \mu } \right)$;
     end while
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2019-03-20
  • 修回日期:  2019-09-30
  • 網(wǎng)絡(luò)出版日期:  2020-01-20
  • 刊出日期:  2020-06-04

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