基于最大间隔准则的鲁棒多流形判别局部图嵌入算法
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摘要
针对现有的多流形人脸识别算法大多直接使用带有噪声的原始数据进行处理,而带有噪声的数据往往会对算法的准确率产生负面影响的问题,提出了一种基于最大间距准则的鲁棒多流形判别局部图嵌入算法(RMMDLGE/MMC)。首先,通过引入一个降噪投影对原始数据进行迭代降噪处理,提取出更加纯净的数据;其次,对数据图像进行分块,建立多流形模型;再次,结合最大间隔准则的思想,寻求最优的投影矩阵使得不同流形上的样本距离尽可能大,同时相同流形上的样本距离尽可能小;最后,计算待识样本流形到训练样本流形的距离进行分类识别。实验结果表明,与表现较好的最大间距准则框架下的多流形局部图嵌入算法(MLGE/MMC)相比,所提算法在添加噪声的ORL、Yale和FERET库上的分类识别率分别提高了1.04、1.28和2.13个百分点,分类效果明显提高。
In most existing multi-manifold face recognition algorithms, the original data with noise are directly processed, but the noisy data often have a negative impact on the accuracy of the algorithm. In order to solve the problem, a Robust Multi-Manifold Discriminant Local Graph Embedding algorithm based on the Maximum Margin Criterion(RMMDLGE/MMC) was proposed. Firstly, a denoising projection was introduced to process the original data for iterative noise reduction, and the purer data were extracted. Secondly, the data image was divided into blocks and a multi-manifold model was established. Thirdly, combined with the idea of maximum margin criterion, an optimal projection matrix was sought to maximize the sample distances on different manifolds while to minimize the sample distances on the same manifold. Finally, the distance from the test sample manifold to the training sample manifold was calculated for classification and identification. The experimental results show that, compared with Multi-Manifold Local Graph Embedding algorithm based on the Maximum Margin Criterion(MLGE/MMC) which performs well, the classification recognition rate of the proposed algorithm is improved by 1.04, 1.28 and 2.13 percentage points respectively on ORL, Yale and FERET database with noise and the classification effect is obviously improved.
In most existing multi-manifold face recognition algorithms, the original data with noise are directly processed, but the noisy data often have a negative impact on the accuracy of the algorithm. In order to solve the problem, a Robust Multi-Manifold Discriminant Local Graph Embedding algorithm based on the Maximum Margin Criterion(RMMDLGE/MMC) was proposed. Firstly, a denoising projection was introduced to process the original data for iterative noise reduction, and the purer data were extracted. Secondly, the data image was divided into blocks and a multi-manifold model was established. Thirdly, combined with the idea of maximum margin criterion, an optimal projection matrix was sought to maximize the sample distances on different manifolds while to minimize the sample distances on the same manifold. Finally, the distance from the test sample manifold to the training sample manifold was calculated for classification and identification. The experimental results show that, compared with Multi-Manifold Local Graph Embedding algorithm based on the Maximum Margin Criterion(MLGE/MMC) which performs well, the classification recognition rate of the proposed algorithm is improved by 1.04, 1.28 and 2.13 percentage points respectively on ORL, Yale and FERET database with noise and the classification effect is obviously improved.
引文
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[7] HE X F,YAN S C,HU Y X,et al.Face recognition using Laplacianfaces [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[8] TENENBAUM J B,SILVA V D,LANGFORD J C.A global geometric framework for nonlinear dimensionality reduction [J].Science,2000,290(5500):2319-2323.
[9] BELKIN M,NIYOGI P.Laplacian eigenmaps for dimensionality reduction and data representation [J].Neural Computation,2003,15(6):1373-1396.
[10] LI B,WANG C,HUANG D S.Supervised feature extraction based on orthogonal discriminant projection [J].Neurocomputing,2009,73(1):191-196.
[11] YAN S,XU D,ZHANG B,et al.Graph embedding and extensions:a general framework for dimensionality reduction[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(1):40-51.
[12] LIN K Z,RONG Y H,WU D,et al.Discriminant locality preserving projections based on neighborhood maximum margin [J].International Journal of Hybrid Information Technology,2014,7(6):165-174.
[13] LI B,HUANG D S,WANG C,et al.Feature extraction using constrained maximum variance mapping [J].Pattern Recognition,2008,41(11):3287-3294.
[14] YANG W K,SUN C Y,ZHANG L.A multi-manifold discriminant analysis method for image feature extraction [J].Pattern Recognition,2011,44(8):1649-1657.
[15] WAN M H,LAI Z H.Multi-manifold Locality Graph Embedding based on the Maximum Margin Criterion (MLGE/MMC) for face recognition [J].IEEE Access,2017,5:9823-9830.
[16] LI H,JIANG T,ZHANG K.Efficient and robust feature extraction by maximum margin criterion [J].IEEE Transactions on Neural Networks,2006,17(1):157-165.
[17] HOU C,NIE F,LI X,et al.Joint embedding learning and sparse regression:a framework for unsupervised feature selection [J].IEEE Transactions on Cybernetics,2014,44(6):793-804.
[18] SHI J,MALIK J.Normalized cuts and image segmentation [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,22(8):888-905.
[19] LU J,TAN Y,WANG G.Discriminative multimanifold analysis for face recognition from a single training sample per person [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):39-51.
[20] TURK M,PENTLAND A.Eigenfaces for recognition [J].Journal of Cognitive Neuroscience,1991,3(1):72-86.
[21] 董西伟,尧时茂,王玉伟,等.基于虚拟样本图像集的多流形鉴别学习算法[J].计算机应用研究,2018,35(6):1871-1878.(DONG X W,YAO S M,WANG Y W,et al.Virtual sample image set based multi manifold discriminant learning algorithm [J].Application Research of Computers,2018,35(6):1871-1878.)
[22] WITTEN I H,HALL M A.Data Mining Practical Machine Learning Tools and Techniques [M].3nd ed.San Francisco,CA:Morgan Kaufmann Publishers,2011:340-345.