浏览全部资源
扫码关注微信
收稿：2018-09-13，
修回：2019-1-11，
纸质出版：2019-07-16
移动端阅览
目的
2
典型相关分析是一种经典的多视图学习方法。为了提高投影方向的判别性能，现有典型相关分析方法通常采用引入样本标签信息的策略。然而，获取样本的标签信息需要付出大量的人力与物力，为此，提出了一种联合标签预测与判别投影学习的半监督典型相关分析算法。
方法
2
将标签预测与模型构建相融合，具体地说，将标签预测融入典型相关分析框架中，利用联合学习框架学得的标签矩阵更新投影方向，进而学得的投影方向又重新更新标签矩阵。标签预测与投影方向的学习过程相互依赖、交替更新，预测标签不断地接近其真实标签，有利于学得最优的投影方向。
结果
2
本文方法在AR、Extended Yale B、Multi-PIE和ORL这4个人脸数据集上分别进行实验。特征维度为20时，在AR、Extended Yale B、Multi-PIE和ORL人脸数据集上分别取得87%、55%、83%和85%识别率。取训练样本中每人2（3，4，5）幅人脸图像为监督样本，提出的方法识别率在4个人脸数据集上均高于其他方法。训练样本中每人5幅人脸图像为监督样本，在AR、Extended Yale B、Multi-PIE和ORL人脸数据集上分别取得94.67%、68%、83%和85%识别率。实验结果表明在训练样本标签信息较少情况下以及特征降维后的维数较低的情况下，联合学习模型使得降维后的数据最大限度地保存更加有效的信息，得到较好的识别结果。
结论
2
本文提出的联合学习方法提高了学习的投影方向的判别性能，能够有效地处理少量的有标签样本和大量的无标签样本的情况以及解决两步学习策略的缺陷。
Objective
2
Canonical correlation analysis (CCA) is a classic method of multi-view learning. Existing CCA-based methods often adopt the strategy of embedding the label information of samples into the models to improve the discriminative capability of the learning projection direction. However
obtaining the label information of data in real life is highly difficult and requires substantial manpower and material resources. In this respect
scholars have proposed the model of semi-supervised canonical correlation analysis
which can utilize limited number of labeled data and a quantity of unlabeled ones for training to learn the projection direction. However
the existing models of semi-supervised canonical correlation analysis adopt a two-step learning strategy. The model is developed after label prediction. Thus
the processes of label prediction and model development are independent. Using the prediction label of the unlabeled sample to construct the model leads to local optimization of the projection direction
thereby affecting the next classification results. This work proposes joint label prediction and discriminant projection learning for semi-supervised canonical correlation analysis to solve the semi-supervised learning problem and shortcomings of the two-step learning strategy.
Method
2
The algorithm combines label prediction with model development. Specifically
the label prediction is integrated into the framework of canonical correlation analysis. The label matrix of the training samples learned by the joint learning framework is used to update the projection direction. Then
the learned projection direction renews the labels of unlabeled data. The learning process of the label prediction and the projection direction depend on each other and are alternately updated. The predicted tag value should be as close as possible to its true value
which is beneficial to learning the optimal projection direction. The optimization of the joint learning framework adopts an alternate iterative strategy to achieve optimal values for the predicted label and projection direction. The discriminant features of the testing images are extracted from the discriminant projection direction acquired by the joint learning framework. Finally
the discriminant features of the testing images are categorized by the classifier.
Result
2
Experiments regarding the proposed algorithm are performed on four face datasets
including AR
Extended Yale B
Multi-PIE
and ORL. The experiment results show that the proposed method can obtain enhanced recognition outcomes only by few features and labeled data. Specifically
three face images of each person in the training samples are selected as supervised samples to analyze the effects of experimental results from the different sample dimensions. Face recognition is high as face image dimension is high in any method. The face recognition rate of the proposed algorithm exhibits significant advantages in low dimension compared with other methods. When the feature dimension is 20
the recognition rates on the AR
Extend-ed Yale B
Multi-PIE
and ORL face datasets are 87%
55%
83%
and 85%
respectively. The 2 (3
4
5) face images of each person in the training samples are selected as supervised samples to analyze the effects of experimental results from the different numbers of labeled data. Face recognition is high as the number of labeled face image is large in any method. Five face images per person in the training sample were supervised samples. The recognition rates on the AR
Extended Yale B
Multi-PIE
and ORL face datasets are 94.67%
68%
83%
and 85%
respectively.
Conclusion
2
The work proposes a joint learning method that render the learning projection direction highly discriminative
which can effectively handle a limited number of labeled data and a quantity of the unlabeled data and solve the shortcomings of the two-step learning strategy. The experiment results on the AR
Extended Yale B
Multi-PIE
and ORL face datasets demonstrate that the recognition rate of the proposed method is significantly higher than those of other methods. This condition occurs when the supervised samples in the training samples are scarce
and the dimensionality of the data features after dimension reduction is low. The convergence of the proposed iterative algorithm is confirmed by experiments. This finding shows that the feature extracted using discriminant projection direction learned by the joint learning model leads data after dimension reduction to keep the information inherent in the data as much as possible. Finally
enhanced classification results can be obtained using the classifier to categorize the extracted features.
Sun S L, Xie X J, Yang M. Multiview uncorrelated discriminant analysis[J]. IEEE Transactions on Cybernetics, 2016, 46(12):3272-3284.[DOI:10.1109/TCYB.2015.2502248]
Shen X B, Sun Q S. Orthogonal multiset canonical correlation analysis based on fractional-order and its application in multiple feature extraction and recognition[J]. Neural Processing Letters, 2015, 42(2):301-316.[DOI:10.1007/s11063-014-9358-5]
Chen X H, Chen S C, Xue H, et al. A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data[J]. Pattern Recognition, 2012, 45(5):2005-2018.[DOI:10.1016/j.patcog.2011.11.008]
Hou C P, Zhang C S, Wu Y, et al. Multiple view semi-supervised dimensionality reduction[J]. Pattern Recognition, 2010, 43(3):720-730.[DOI:10.1016/j.patcog.2009.07.015]
Gönen M, Alpaydın E. Multiple kernel learning algorithms[J]. The Journal of Machine Learning Research, 2011, 12:2211-2268.
Cortes C, Mohri M, Rostamizadeh A. Two-stage learning kernel algorithms[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel: ICML, 2010: 239-246.
Zhou Z H, Li M. Semi-supervised learning by disagreement[J]. Knowledge and Information Systems, 2010, 24(3):415-439.
Yu C C, Liu Y, Tan L, et al. Multi-view semi-supervised collaboration classification algorithm with combination of agreement and disagreement label rules[J]. Journal of Computer Applications, 2013, 33(11):3090-3093.
于重重, 刘宇, 谭励, 等.组合标记的多视图半监督协同分类算法[J].计算机应用, 2013, 33(11):3090-3093. [DOI:10.11772/j.issn.1001-9081.2013.11.3090]
Hardoon D R, Szedmak S R, Shawe-Taylor J. Canonical correlation analysis:an overview with application to learning methods[J]. Neural Computation, 2004, 16(12):2639-2664.[DOI:10.1162/0899766042321814]
Hotelling H. Relations between two sets of variates[J]. Biometrika, 1936, 28(3-4):321-377.[DOI:10.2307/2333955]
Zheng W M, Zhou X Y, Zou C R, et al. Facial expression recognition using kernel canonical correlation analysis[J]. IEEE Transactions on Neural Networks, 2006, 17(1):233-238.[DOI:10.1109/TNN.2005.860849]
Nielsen A A. Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data[J]. IEEE Transactions on Image Processing, 2002, 11(3):293-305.[DOI:10.1109/83.988962]
Wang F S, Zhang D Q. A new locality-preserving canonical correlation analysis algorithm for multi-view dimensionality reduction[J]. Neural Processing Letters, 2013, 37(2):135-146.[DOI:10.1007/s11063-012-9238-9]
Ko W J, Yu J Y, Chen W Y, et al. Enhanced canonical correlation analysis with local density for cross-domain visual classification[C]//Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. New Orleans, LA, USA: IEEE, 2017: 1757-1761.[ DOI:10.1109/ICASSP.2017.7952458 http://dx.doi.org/10.1109/ICASSP.2017.7952458 ]
Sun T K, Chen S C, Yang J Y, et al. A novel method of combined feature extraction for recognition[C]//Proceedings of the 8th IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008: 1043-1048.[ DOI:10.1109/ICDM.2008.28 http://dx.doi.org/10.1109/ICDM.2008.28 ]
Peng Y, Zhang D Q, Zhang J C. A new canonical correlation analysis algorithm with local discrimination[J]. Neural Processing Letters, 2010, 31(1):1-15.[DOI:10.1007/s11063-009-9123-3]
Xia J M, Yang J A, Kang K. Canonical correlation analysis based on local sparse representation and linear discriminative analysis[J]. Control and Decision, 2014, 29(7):1279-1284.
夏建明, 杨俊安, 康凯.基于局部稀疏表示和线性鉴别分析的典型相关分析[J].控制与决策, 2014, 29(7):1279-1284. [DOI:10.13195/j.kzyjc.2013.0444]
Shen X B, Sun Q S. A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction[J]. Journal of Visual Communication and Image Representation, 2014, 25(8):1894-1904.[DOI:10.1016/j.jvcir.2014.09.004]
Wan J W, Wang H Y, Yang M. Cost sensitive semi-supervised canonical correlation analysis for multi-view dimensionality reduction[J]. Neural Processing Letters, 2017, 45(2):411-430.[DOI:10.1007/s11063-016-9532-z]
Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905.[DOI:10.1109/34.868688]
Von Luxburg U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4):395-416.[DOI:10.1007/s11222-007-9033-z]
Yang Y, Shen H T, Nie F P, et al. Nonnegative spectral clustering with discriminative regularization[C]//Proceedings of the 25th AAAI Conference on Artificial Intelligence. San Francisco, California: ACM, 2011: 555-560.
Yang Y, Yang Y, Shen H T, et al. Discriminative nonnegative spectral clustering with out-of-sample extension[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8):1760-1771.[DOI:10.1109/TKDE.2012.118]
Lee D D, Seung H S. Algorithms for non-negative matrix factorization[C]//Advances in Neural Information Processing Systems. British Columbia, Canada: MIT Press, 2001: 556-562.
Kursun O, Alpaydin E. Canonical correlation analysis for multiview semisupervised feature extraction[M]//Rutkowski L, Scherer R, Tadeusiewicz R, et al. Artificial Intelligence and Soft Computing. Heidelberg: Springer, 2010: 430-436.[ DOI:10.1007/978-3-642-13208-7_54 http://dx.doi.org/10.1007/978-3-642-13208-7_54 ]
Peng Y, Zhang D Q. Semi-supervised canonical correlation analysis algorithm[J]. Journal of Software, 2008, 19(11):2822-2832.
彭岩, 张道强.半监督典型相关分析算法[J].软件学报, 2008, 19(11):2822-2832.
相关文章
相关作者
相关机构
京公网安备11010802024621
