图正则化字典对学习的轻度认知功能障碍预测
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摘要
针对字典对学习(DPL)方法只考虑了同类子字典的重构误差和不同类表示系数的稀疏性,没有考虑图像间的几何近邻拓扑关系的问题。通过近邻保持使得在同类近邻投影系数之间的距离较小,而不同类投影系数之间的距离大,能够有效提高字典对学习算法的分类性能,基于此提出了基于几何近邻拓扑关系的图正则化的字典对学习(GDPL)算法。在ADNI1数据集上对轻度认知功能障碍预测的实验表明,使用GDPL算法学习的编码系数作为特征预测的准确率(ACC)和ROC曲线下的面积(AUC)比使用结合生物标志作为特征预测的准确率提高了2%～6%,使用GDPL算法比DPL算法的实验结果也有提高。
Aiming at dictionary pair learning(DPL) methods only consider the reconstruction error of a sub-dictionary from the same class and the sparseness of coefficients from different classes, and do not consider the geometric neighborhood topological relationships between images. To improve the classification ability of DPL algorithms, we propose a DPL with graph regularization(GDPL) algorithm based on geometric neighborhood topological relationships. This algorithm is based on the idea that keeping the neighborhood relationship makes the distance between the neighborhood projection coefficients of the same kind small, while the distance between projection coefficients of different kinds is large. Experiments on mild cognitive impairment prediction using the ADNI1 dataset show that the coding coefficient learned from the GDPL algorithm is 2%～6% higher than that which uses the combined biomarker as feature prediction,according to accuracy(ACC) and area under curve(AUC) metrics. Moreover, the experimental result obtained using GDPL is also better than that obtained using DPL algorithm.
Aiming at dictionary pair learning(DPL) methods only consider the reconstruction error of a sub-dictionary from the same class and the sparseness of coefficients from different classes, and do not consider the geometric neighborhood topological relationships between images. To improve the classification ability of DPL algorithms, we propose a DPL with graph regularization(GDPL) algorithm based on geometric neighborhood topological relationships. This algorithm is based on the idea that keeping the neighborhood relationship makes the distance between the neighborhood projection coefficients of the same kind small, while the distance between projection coefficients of different kinds is large. Experiments on mild cognitive impairment prediction using the ADNI1 dataset show that the coding coefficient learned from the GDPL algorithm is 2%～6% higher than that which uses the combined biomarker as feature prediction,according to accuracy(ACC) and area under curve(AUC) metrics. Moreover, the experimental result obtained using GDPL is also better than that obtained using DPL algorithm.
引文
[1]邵冬华,施志刚,史军杰.二次近邻稀疏重构法及人脸识别[J].重庆邮电大学学报(自然科学版), 2017, 29(6):844–850.SHAO Donghua, SHI Zhigang, SHI Junjie. Sparse reconstruction algorithm based on secondary nearest neighbor and face recognition[J]. Journal of Chongqing university of posts and telecommunications(natural science edition),2017, 29(6):844–850.
[2]王佐成,杨娟,薛丽霞.基于二进神经网络的稀疏编码架构的图像无损压缩新方法[J].重庆邮电大学学报(自然科学版), 2017, 29(3):371–376.WANG Zuocheng, YANG Juan, XUE Lixia. A novel lossless image compression scheme based on binary neural networks with sparse coding[J]. Journal of Chongqing University of Posts and Telecommunications(natural science edition), 2017, 29(3):371–376.
[3]WRIGHT J, MA Yi, MAIRAL J, et al. Sparse representation for computer vision and pattern recognition[J]. Proceedings of the IEEE, 2010, 98(6):1031–1044.
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[5]AHARON M, ELAD M, BRUCKSTEIN A. rmK-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE transactions on signal processing, 2006, 54(11):4311–4322.
[6]YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Fisher discrimination dictionary learning for sparse representation[C]//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain, 2011:543–550.
[7]JIANG Zhuolin, LIN Zhe, DAVIS L S. Label consistent K-SVD:learning a discriminative dictionary for recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(11):2651–2664.
[8]LU Jiwen, WANG Gang, DENG Weihong, et al. Simultaneous feature and dictionary learning for image set based face recognition[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland,2014:265–280.
[9]YANG Yang, HUANG Zi, YANG Yi, et al. Local image tagging via graph regularized joint group sparsity[J]. Pattern recognition, 2013, 46(5):1358–1368.
[10]GU Shuhang, ZHANG Lei, ZUO Wangmeng, et al. Projective dictionary pair learning for pattern classification[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal,Canada, 2014:793–801.
[11]CUINGNET R, GERARDIN E, TESSIERAS J, et al.Automatic classification of patients with Alzheimer's disease from structural MRI:a comparison of ten methods using the ADNI database[J]. NeuroImage, 2011, 56(2):766–781.
[12]CHEN Yefei, SU Jianbo. Sparse embedded dictionary learning on face recognition[J]. Pattern recognition, 2017,64:51–59.
[13]MAIRAL J, BACH F, PONCE J, et al. Supervised dictionary learning[J]. Paris:INRIA, 2008:1–8.
[14]TONG Tong, GAO Qinquan, GUERRERO R, et al. A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease[J].IEEE transactions on biomedical engineering, 2017,64(1):155–165.
[15]WYMAN B T, HARVEY D J, CRAWFORD K, et al.Standardization of analysis sets for reporting results from ADNI MRI data[J]. Alzheimer's&dementia, 2013, 9(3):332–337.
[16]MORADI E, PEPE A, GASER C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. NeuroImage, 2015, 104:398–412.
[17]JANOU?OVáE, VOUNOU M, WOLZ R, et al. Biomarker discovery for sparse classification of brain images in Alzheimer's disease[J]. Annals of the BMVA, 2012,2012(2):1–11.
[2]王佐成,杨娟,薛丽霞.基于二进神经网络的稀疏编码架构的图像无损压缩新方法[J].重庆邮电大学学报(自然科学版), 2017, 29(3):371–376.WANG Zuocheng, YANG Juan, XUE Lixia. A novel lossless image compression scheme based on binary neural networks with sparse coding[J]. Journal of Chongqing University of Posts and Telecommunications(natural science edition), 2017, 29(3):371–376.
[3]WRIGHT J, MA Yi, MAIRAL J, et al. Sparse representation for computer vision and pattern recognition[J]. Proceedings of the IEEE, 2010, 98(6):1031–1044.
[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, 2009, 31(2):210–227.
[5]AHARON M, ELAD M, BRUCKSTEIN A. rmK-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE transactions on signal processing, 2006, 54(11):4311–4322.
[6]YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Fisher discrimination dictionary learning for sparse representation[C]//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain, 2011:543–550.
[7]JIANG Zhuolin, LIN Zhe, DAVIS L S. Label consistent K-SVD:learning a discriminative dictionary for recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(11):2651–2664.
[8]LU Jiwen, WANG Gang, DENG Weihong, et al. Simultaneous feature and dictionary learning for image set based face recognition[C]//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland,2014:265–280.
[9]YANG Yang, HUANG Zi, YANG Yi, et al. Local image tagging via graph regularized joint group sparsity[J]. Pattern recognition, 2013, 46(5):1358–1368.
[10]GU Shuhang, ZHANG Lei, ZUO Wangmeng, et al. Projective dictionary pair learning for pattern classification[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal,Canada, 2014:793–801.
[11]CUINGNET R, GERARDIN E, TESSIERAS J, et al.Automatic classification of patients with Alzheimer's disease from structural MRI:a comparison of ten methods using the ADNI database[J]. NeuroImage, 2011, 56(2):766–781.
[12]CHEN Yefei, SU Jianbo. Sparse embedded dictionary learning on face recognition[J]. Pattern recognition, 2017,64:51–59.
[13]MAIRAL J, BACH F, PONCE J, et al. Supervised dictionary learning[J]. Paris:INRIA, 2008:1–8.
[14]TONG Tong, GAO Qinquan, GUERRERO R, et al. A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease[J].IEEE transactions on biomedical engineering, 2017,64(1):155–165.
[15]WYMAN B T, HARVEY D J, CRAWFORD K, et al.Standardization of analysis sets for reporting results from ADNI MRI data[J]. Alzheimer's&dementia, 2013, 9(3):332–337.
[16]MORADI E, PEPE A, GASER C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. NeuroImage, 2015, 104:398–412.
[17]JANOU?OVáE, VOUNOU M, WOLZ R, et al. Biomarker discovery for sparse classification of brain images in Alzheimer's disease[J]. Annals of the BMVA, 2012,2012(2):1–11.