应用于人脸图像识别的邻域保持极限学习机
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摘要
极限学习机广泛应用于人脸识别领域。传统的极限学习机算法因在少量标签样本上进行训练,容易发生学习过程不充分问题,同时在学习过程中往往忽略了样本内在的几何结构,影响其对人脸识别的分类能力。受流形学习思想的启发,提出一种邻域保持极限学习机算法。该算法保持数据最本质的结构和同类数据的判别信息,利用最小化类内散度矩阵来提高极限学习机整体的分类性能。通过人脸数据集上的多次实验结果表明,该算法的人脸识别准确率高于其他算法,更能有效地进行分类识别。
Extreme learning machine is widely used in the field of face recognition. The traditional extreme learning algorithm easily leads to the problem of insufficient learning process due to training on a small number of labeled samples.At the same time, the intrinsic geometry of the sample is often ignored in the learning process, which affects its classification ability for face recognition. Inspired by the idea of manifold learning, a neighborhood preserving extreme learning algorithm is proposed. The algorithm preserves the most essential structure of data and discriminant information of intraclass data, and minimizes the intra-class divergence matrix to improve the classification performance of the extreme learning machine. The experimental results on face datasets show that the algorithm has a higher accuracy of face recognition than other algorithms and is more effective in classification and recognition.
Extreme learning machine is widely used in the field of face recognition. The traditional extreme learning algorithm easily leads to the problem of insufficient learning process due to training on a small number of labeled samples.At the same time, the intrinsic geometry of the sample is often ignored in the learning process, which affects its classification ability for face recognition. Inspired by the idea of manifold learning, a neighborhood preserving extreme learning algorithm is proposed. The algorithm preserves the most essential structure of data and discriminant information of intraclass data, and minimizes the intra-class divergence matrix to improve the classification performance of the extreme learning machine. The experimental results on face datasets show that the algorithm has a higher accuracy of face recognition than other algorithms and is more effective in classification and recognition.
引文
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[15] Liu S,Feng L,Xiao Y.Robust activation function and its application:semi-supervised kernel extreme learning method[J].Neurocomputing,2014,144:318-328.
[16] Huang G B.An insight into extreme learning machines:random neurons,random features and kernels[J].Cognitive Computation,2014,6(3):376-390.
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[2] Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:a new learning scheme of feedforward neural networks[C]//Proceedings of the Neural Networks,2005,2:985-990.
[3] Zhang C,Xia X S,Liu B.Deformed kernel based extreme learning machine[J].Journal of Computers,2013,8(6):1602-1609.
[4] Cortes C,Vapnik V.Support vector networks[J].Mach Learn,1995,20(3):273-297.
[5] Rumelhart D,Hinton G,Williams R.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536.
[6] Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2012,42(2):513-529.
[7] Huang G,Huang G B,Song S,et al.Trends in extreme learning machines[J].Neural Networks,2015,61(C):32-48.
[8] Yang J,Xie S,Yoon S,et al.Fingerprint matching based on extreme learning machine[J].Neural Computing and Applications,2013,22(3/4):435-445.
[9] Zhao J,Zhou Z,Cao F.Human face recognition based on ensemble of polyharmonic extreme learning machine[J].Neural Computing and Applications,2014:24(6):1317-1326.
[10] Huang G B,Song S,Gupta J N,et al.Semi-supervised and unsupervised extreme learning machines[J].IEEE Transactions on Cybernetics,2014,44(12):2405-2417.
[11] Liu B,Xia S X,Meng F R,et al.Manifold regularized extreme learning machine[J].Neural Computing and Applications,2015.
[12] Li J,Du Q,Li W,et al.Optimizing extreme learning machine for hyperspectral image classification[J].Journal of Applied Remote Sensing,2015,9(1):097296.
[13] Peng Y,Wang S,Long X,et al.Discriminative graph regularized extreme learning machine and its application to face recognition[J].Neurocomputing,2015,149:340-353.
[14] He X,Cai D,Yan S,et al.Neighborhood preserving embedding[C]//Tenth IEEE International Conference on Computer Vision,2005,2(23):1208-1213.
[15] Liu S,Feng L,Xiao Y.Robust activation function and its application:semi-supervised kernel extreme learning method[J].Neurocomputing,2014,144:318-328.
[16] Huang G B.An insight into extreme learning machines:random neurons,random features and kernels[J].Cognitive Computation,2014,6(3):376-390.
[17] Jolliffe I.Principal component analysis[M].[S.l.]:John Wiley&Sons,2005.
[18] Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.