计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 137-143.doi: 10.11896/jsjkx.190600090

• 计算机图形学&多媒体 • 上一篇    下一篇

融合极端学习机的判别性分析字典学习算法

王军浩, 闫德勤, 刘德山, 邢钰佳   

  1. 辽宁师范大学计算机与信息技术学院 辽宁 大连116033
  • 收稿日期:2019-06-18 出版日期:2020-05-15 发布日期:2020-05-19
  • 通讯作者: 闫德勤(yandeqin@163.com)
  • 作者简介:lnnu_junhaowang@163.com
  • 基金资助:
    辽宁省自然科学基金(20170540574);辽宁省教育厅科学研究项目(LJ2019014)

Algorithm with Discriminative Analysis Dictionary Learning by Fusing Extreme Learning Machine

WANG Jun-hao, YAN De-qin, LIU De-shan, XING Yu-jia   

  1. School of Computer and Information Technology,Liaoning Normal University,Dalian,Liaoning 116033,China
  • Received:2019-06-18 Online:2020-05-15 Published:2020-05-19
  • About author:WANG Jun-hao,born in 1994,postgra-duate.His main research interests include machine learning,dictionary learning and remote sensing image classification.
    YAN De-qin,born in 1962,Ph.D,professor.His main research interests include machine learning,dictionary learning,deep learning and remote sensing image classification.
  • Supported by:
    This work was supported by the Natural Science Foundation of Liaoning Province,China(20170540574) and Scientific Research Project of LiaoningEducation Department(LJ2019014)

摘要: 研究表明,端学习机和判别性字典学习算法在图像分类领域极具有高效和准确的优势。然而,这两种方法也具有各自的缺点,极端学习机对噪声的鲁棒性较差,判别性字典学习算法在分类过程中耗时较长。为统一这种互补性以提高分类性能,文中提出了一种融合极端学习机的判别性分析字典学习模型。该模型利用迭代优化算法学习最优的判别性分析字典和极端学习机分类器。为验证所提算法的有效性,利用人脸数据集进行分类。实验结果表明,与目前较为流行的字典学习算法和极端学习机相比,所提算法在分类过程中具有更好的效果。

关键词: 分析字典学习, 极端学习机, 判别性字典学习

Abstract: Recent researches have shown that the speed advantage of extreme learning machine (ELM) and the accuracy advantage of discriminative dictionary learning (DDL) in the area of image classification.However these two methods have their respective drawbacks,in general,ELM is known to be less robust to noise while DDL is known to be time-consuming.In order to unify such mutual complementarity and further enhance the classification performance,we propose a discriminative analysis dictionary learning fusing extreme learning machine model in this paper.More precisely,the iterative optimization algorithm is used to learn the most optimal discriminative analysis dictionary and extreme learning machine classifier.In order to verify the effect of the proposed algorithm,the face data is used for classification.Experiments demonstrate that our method achieves a better performance than the state-of-the-art dictionary learning algorithms and extreme learning machine in a variety of image classification tasks.

Key words: Analysis dictionary learning, Discriminative dictionary learning, Extreme learning machine

中图分类号: 

  • TP391
[1]ELAD M,AHARON M.Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[2]MAIRAL J,ELAD M,SAPIRO G.Sparse Representation for Color Image Restoration[J].IEEE Transactions on Image Processing,2007,17(1):53-69.
[3]RANZATO M,POULTNEY C,CHOPRA S,et al.EfficientLearning of Sparse Representations with an Energy-Based Model[C]//Advances in Neural Information Processing Systems.2006:1137-1144.
[4]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(2):210-227.
[5]YANG J,YU K,GONG Y,et al.Linear spatial pyramid matching using sparse coding for image classification[C]//Cvpr.2009:1794-1801.
[6]JIANG Z,LIN Z,DAVIS L S.Label Consistent K-SVD:Lear-ning a Discriminative Dictionary for Recognition[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2013,35(11):2651-2664.
[7]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[8]ZHANG Q,LI B.Discriminative K-SVD for dictionary learning in face recognition[C]//2010 IEEE Computer Society Confe-rence on Computer Vision and Pattern Recognition.IEEE,2010:2691-2698.
[9]CAI S,ZUO W,ZHANG L,et al.Support vector guided dictio-nary learning[C]//European Conference on Computer Vision.Cham:Springer,2014:624-639.
[10]RUBINSTEIN R,PELEG T,ELAD M.Analysis K-SVD:A Dictionary-Learning Algorithm for the Analysis Sparse Model[J].IEEE Transactions on Signal Processing,2013,61(3):661-677.
[11]SHEKHAR S,PATEL V M,CHELLAPPA R.Analysis sparse coding models for image-based classification[C]//2014 IEEE International Conference on Image Processing (ICIP).IEEE,2014:5207-5211.
[12]TANG W,PANAHI A,KRIM H,et al.Analysis DictionaryLearning:An Efficient and Discriminative Solution[J].arXiv:1903.03058,2019.
[13]PENG J,JIANG X,CHEN N,et al.Local adaptive joint sparse representation for hyperspectral image classification[J].Neurocomputing,2019,334:239-248.
[14]XU Y,LI Z,ZHANG B,et al.Sample diversity,representation effectiveness and robust dictionary learning for face recognition[J].Information Sciences,2017,375:171-182.
[15]LI Z,LAI Z,XU Y,et al.A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification[J].IEEE Transactions on Neural Networks and Learning Systems,2015:1-16.
[16]GUO J,GUO Y,KONG X,et al.Discriminative analysis dictio-nary learning [C]//Thirtieth AAAI Conference on Artificial Intelligence.2016.
[17]WANG J,GUO Y,GUO J,et al.Synthesis linear classifier based analysis dictionary learning for pattern classification[J].Neurocomputing,2017,238:103-113.
[18]WANG Q,GUO Y,GUO J,et al.Synthesis K-SVD based analysis dictionary learning for pattern classification[J].Multimedia Tools and Applications,2018,77(13):17023-17041.
[19]TANG W,PANAHI A,KRIM H,et al.Structured analysis dictionary learning for image classification[C]//2018 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2018:2181-2185.
[20]WEI Y,JIAO L,LIU F,et al.Fast DDL Classification for SAR Images with l1,∞ Constraint[J].IEEE Access,2019,7:68991-69006.
[21]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma-chine:a new learning scheme of feedforward neural networks[C]//2004 IEEE International Joint Conference on Neural Networks.2004:985-990.
[22]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma-chine:theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501.
[23]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Cognitive Mode-ling,1988,5(3):1.
[24]LIU X,LIN S,FANG J,et al.Is extreme learning machine feasible? A theoretical assessment (Part I)[J].IEEE Transactions on Neural Networks and Learning Systems,2014,26(1):7-20.
[25]LIN S,LIU X,FANG J,et al.Is extreme learning machine feasible? A theoretical assessment (Part II)[J].IEEE Transactions on Neural Networks and Learning Systems,2014,26(1):21-34.
[26]YAN D,CHU Y,ZHANG H,et al.Information discriminative extreme learning machine[J].Soft Computing,2018,22(2):677-689.
[27]LI Q,LIU Y,WANG S,et al.Image Classification Using Low-Rank Regularized Extreme Learning Machine[J].IEEE Access,2019,7:877-883.
[28]PARK Y,YANG H S.Convolutional neural network based on an extreme learning machine for image classification[J].Neurocomputing,2019,339:66-76.
[29]SUN Y,LI B,YUAN Y,et al.Big graph classification frameworks based on Extreme Learning Machine[J].Neurocompu-ting,2019,330:317-327.
[30]CAO J,ZHANG K,LUO M,et al.Extreme learning machine and adaptive sparse representation for image classification[J].Neural Networks,2016,81:91-102
[31]YANG J,YU K,HUANG T.Supervised translation-invariantsparse coding[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:3517-3524.
[32]PHAM D S,VENKATESH S.Joint learning and dictionary construction for pattern recognition[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2008:1-8.
[33]MAIRAL J,PONCE J,SAPIRO G,et al.Supervised dictionary learning[C]//Advances in Neural Information Processing Systems.2009:1033-1040.
[34]YANG C,LIU H,WANG S,et al.Remote sensing image classification using extreme learning machine-guided collaborative coding[J].Multidimensional Systems and Signal Processing,2017,28(3):835-850.
[35]SONG X,CHEN Y,FENG Z H,et al.Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning[J].Pattern Recognition,2019,88:127-138.
[36]WANG H H,TU C W,CHIANG C K.Sparse representation for image classification via paired dictionary learning[J].Multimedia Tools and Applications,2019,78(12):16945-16963.
[37]SONG J,XIE X,SHI G,et al.Multi-layer discriminative dictio-nary learning with locality constraint for image classification[J].Pattern Recognition,2019,91:135-146.
[38]RAKOTOMAMONJY A.Applying alternating direction method of multipliers for constrained dictionary learning[J].Neurocomputing,2013,106:126-136.
[39]HE R,ZHENG W S,TAN T,et al.Half-quadratic-based iterative minimization for robust sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,36(2):261-275.
[40]YAGHOOBI M,BLUMENSATH T,DAVIES M E.Dictionary learning for sparse approximations with the majorization method[J].IEEE Transactions on Signal Processing,2009,57(6):2178-2191.
[41]GEORGHIADES A S,BELHUMEUR P N,KRIEGMAN D J.From few to many:Illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2001(6):643-660.
[42]MARTINEZ A,BENAVENTE R.The AR face database:24 CVC Technical Report[R].1998.
[43]HUANG G B,MATTAR M,BERG T,et al.Labeled faces inthe wild:A database forstudying face recognition in unconstrained environments[C]//Workshop on Faces in ‘Real-Life' Images:Detection,Alignment,and Recognition.2008.
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