Computer Science ›› 2021, Vol. 48 ›› Issue (10): 107-113.doi: 10.11896/jsjkx.200900178
• Artificial Intelligence • Previous Articles Next Articles
FAN Lian-xi1, LIU Yan-bei2,3, WANG Wen2, GENG Lei2, WU Jun1, ZHANG Fang2, XIAO Zhi-tao2
CLC Number:
[1]WEINER M W,VEITCH D P,AISEN P S,et al.Recent publications from the alzheimer's disease neuroimaging initiative:reviewing progress toward improved ad clinical trials[J].Alzheimer's & Dementia,2017,13(4):1-85. [2]ZHANG J,YUE G,GAO Y,et al.Detecting anatomical landmarks for fast Alzheimer's disease diagnosis[J].IEEE Transactions on Medical Imaging,2016,35(12):2524-2533. [3]LIAN C,LIU M,ZHANG J,et al.Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's Disease diagnosis using structural MRI[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(4):880-893. [4]LIN W,YUAN J,FENG C,et al.Computer-Aided Diagnosis of Alzheimer's Disease Based on Extreme Learning Machine[J].Chinese Journal of Biomedical Engineering,2020,39(3):288-294. [5]ZENG A,ZOU C,PAN D.Diagnosis of Alzheimer's diseasebased on 3D convolutional neural network-regions of interest[J].Journal of Biomedical Engineering Research,2020,39(2):133-138,144. [6]ZHANG D,WANG Y,ZHOU L,et al.Multimodal classification of alzheimer's disease and mild cognitive impairment[J].Neuroimage,2011,55(3):856-867. [7]KUMAR A,RAI P,DAUME H.Co-regularized multi-viewspectral clustering[C]//Advances in Neural Information Processing Systems.2011:1413-1421. [8]HOTELLING H.Relations between two sets of variates[M]//Breakthroughs in Statistics.Springer,New York,NY,1992:162-190. [9]AKAHO S.A kernel method for canonical correlation analysis[J].arXiv preprint cs/0609071,2006. [10]ANDREW G,ARORA R,BILMES J,et al.Deep canonical correlation analysis[C]//International Conference on Machine Learning.2013:1247-1255. [11]ZHU X,SUK H I,SHEN D.Multi-modality canonical featureselection for alzheimer's disease diagnosis[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.2014:162-169. [12]ZHU X,SUK H I,LEE S W,et al.Canonical feature selection for joint regression and multi-class identification in alzheimer's disease diagnosis[J].Brain Imaging and Behavior,2016,10(3):818-828. [13]ZIEN A,ONG C S.Multiclass multiple kernel learning[C]//International Conference on Machine Learning.2007:1191-1198. [14]LIU X,ZHU X,LI M,et al.Multiple kernel k-means with incomplete kernels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019. [15]ZHOU T,LIU M,THUNG K H,et al.Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data[J].IEEE Transac-tions on Medical Imaging,2019,38(10):2411-2422. [16]SHI Y,SUK H I,GAO Y,et al.Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis[J].IEEE Transac-tions on Neural Networks and Learning Systems,2019,31(1):186-200. [17]LI C,FAN C.Classification and prediction of Alzheimer'sdisease based on machine learning[J].Chinese Journal of Medical Physics,2020,37(3):379-384. [18]LIU M,ZHANG J,ADELI E,et al.Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis[J].IEEE Transactions on Biomedical Engineering,2018,66(5):1195-1206. [19]PAN Z J,LIU N,ZHANG W,et al.MTHAM:MultitaskDisease Progression Modeling Based on Hierarchical Attention Mechanism[J].Computer Science,2020,47(9):185-189. [20]VON LUXBURG U.A tutorial on spectral clustering[J].Statistics and Computing,2007,17(4):395-416. [21]STEINWART I.Fully adaptive density-based clustering[J].The Annals of Statistics,2015,43(5):2132-2167. [22]ZHANG C,ADELI E,ZHOU T,et al.Multi-layer multi-view classification for alzheimer's disease diagnosis[C]//AAAI Conference on Artificial Intelligence.2018. [23]SUN S.A survey of multi-view machine learning[J].Neural Computing and Applications,2013,23(7/8):2031-2038. [24]SA V R D.Spectral clustering with two views[C]//ICMLWorkshop on Learning with Multiple Views.2005:20-27. [25]XU J,HAN J,NIE F.Discriminatively embedded k-means for multi-view clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:5356-5364. [26]WANG H,WENG C,YUAN J.Multi-feature spectral clustering with minimax optimization[C]//IEEE Conference on Computer Vision and Pattern Recognition.2014:4106-4113. [27]ELHAMIFAR E,VIDAL R.Sparse subspace clustering:algorithm,theory,and applications[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2765-2781. [28]HU H,LIN Z,FENG J,et al.Smooth representation clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition.2014:3834-3841. [29]JIAO L,YANG S,LIU F,et al.Seventy Years beyond Neural Networks:Retrospect and Prospect[J].Chinese Journal of Computers,2016,8(39):1697-1716. [30]WANG W,ARORA R,LIVESCU K,et al.On deep multi-view representation learning[C]//International Conference on Machine Learning.2015:1083-1092. [31]ZHANG C,FU H,HU Q,et al.Flexible multi-view dimensiona-lity co-reduction[J].IEEE Transactions on Image Processing,2016,26(2):648-659. |
[1] | SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69. |
[2] | HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82. |
[3] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[4] | LI Zong-min, ZHANG Yu-peng, LIU Yu-jie, LI Hua. Deformable Graph Convolutional Networks Based Point Cloud Representation Learning [J]. Computer Science, 2022, 49(8): 273-278. |
[5] | HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing. Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation [J]. Computer Science, 2022, 49(6A): 407-411. |
[6] | JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139. |
[7] | WANG Ying-li, JIANG Cong-cong, FENG Xiao-nian, QIAN Tie-yun. Time Aware Point-of-interest Recommendation [J]. Computer Science, 2021, 48(9): 43-49. |
[8] | ZHAO Jin-long, ZHAO Zhong-ying. Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network [J]. Computer Science, 2021, 48(8): 72-79. |
[9] | YE Hong-liang, ZHU Wan-ning, HONG Lei. Music Style Transfer Method with Human Voice Based on CQT and Mel-spectrum [J]. Computer Science, 2021, 48(6A): 326-330. |
[10] | YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin. Humans-Cyber-Physical Ontology Fusion of Industry Based on Representation Learning [J]. Computer Science, 2021, 48(5): 190-196. |
[11] | QIAN Sheng-sheng, ZHANG Tian-zhu, XU Chang-sheng. Survey of Multimedia Social Events Analysis [J]. Computer Science, 2021, 48(3): 97-112. |
[12] | WANG Xing , KANG Zhao. Smooth Representation-based Semi-supervised Classification [J]. Computer Science, 2021, 48(3): 124-129. |
[13] | WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning [J]. Computer Science, 2021, 48(2): 207-211. |
[14] | LI Xin-chao, LI Pei-feng, ZHU Qiao-ming. Directed Network Representation Method Based on Hierarchical Structure Information [J]. Computer Science, 2021, 48(2): 100-104. |
[15] | FU Kun, ZHAO Xiao-meng, FU Zi-tong, GAO Jin-hui, MA Hao-ran. Deep Network Representation Learning Method on Incomplete Information Networks [J]. Computer Science, 2021, 48(12): 212-218. |
|