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12-h abstinence-induced functional connectivity density changes and craving in young smokers: a resting-state study.
Brain imaging and behavior, 2019, 13(4): 953-962.
Zhenjun Tang, Lv Chen, Xianquan Zhang, Shichao Zhang.
Robust Image Hashing with Tensor Decomposition.
IEEE Trans. Knowl. Data Eng. 31(3): 549-560 (2019)
Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang.
Low-Rank Sparse Subspace for Spectral Clustering.
IEEE Trans. Knowl. Data Eng. 31(8): 1532-1543 (2019)
Xiaofeng Zhu, Shichao Zhang, Wei He, Rongyao Hu, Cong Lei, Pengfei Zhu.
One-Step Multi-View Spectral Clustering.
IEEE Trans. Knowl. Data Eng. 31(10): 2022-2034 (2019)
Chengyuan Zhang, Lei Zhu, Shichao Zhang.
PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification.
CoRR abs/1906.01792 (2019)
Miao Cheng, Weibin Yang, Yonggang Li, Shichao Zhang, Ah Chung Tsoi, Yuan Yan Tang.
Sequential Pattern Learning via Kernel Alignment.
ICACI 2019: 50-55
吴林，文国秋*，童涛，谭马龙，杜婷婷.
局部PCA与k近邻相结合的谱聚类算法.
计算机工程与设计, 2019(8).
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基于mutualKNN和标准化的谱聚类算法.
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童涛，文国秋，谭马龙，吴林，杜婷婷.
基于LPCA的谱聚类算法.
计算机应用研究, 2019(11).
Zhenjun Tang, Xuelong Li, Xianquan Zhang, Shichao Zhang, Yumin Dai
Image hashing with color vector angle.
Neurocomputing 308: 147-158 (2018)
Zhenyun Deng, Shichao Zhang, Lifeng Yang, Ming Zong, Debo Cheng.
Sparse sample self-representation for subspace clustering.
Neural Computing and Applications 29(1): 43-49 (2018)
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Unsupervised feature selection by self-paced learning regularization.
Pattern Recognition Letters, 2018:S0167865518302782-.
Tan M, Zhang S, Wu L.
Mutual kNN based spectral clustering.
Neural Computing and Applications, 2018:1-8.(SCI)
Tong T, Zhu X, Du T.
Connected graph decomposition for spectral clustering.
Multimedia Tools and Applications, 2018(9):1-13.(SCI)
Lin W, Zhu X, Tao T.
Global and local clustering with kNN and local PCA.
Multimedia Tools & Applications, 2018:1-12.(SCI)
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Dynamic graph learning for spectral feature selection.
Multimedia Tools and Applications, 2018, 77(22): 29739-29755.
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A novel kNN algorithm with data-driven k parameter computation.
Pattern Recognition Letters, 2018, 109: 44-54.
Zhu Y, Zhang X, Hu R, et al.
Adaptive structure learning for low-rank supervised feature selection.
Pattern Recognition Letters, 2018, 109: 89-96.
Fang Y, Li Y, Lei C, et al.
Hypergraph expressing low-rank feature selection algorithm.
Multimedia Tools and Applications, 2018, 77(22): 29551-29572.
Zhang S, Cheng D, Hu R, et al.
Supervised feature selection algorithm via discriminative ridge regression.
World Wide Web, 2018, 21(6): 1545-1562.
Deng Z, Zhu X, Cheng D, et al.
Efficient kNN classification algorithm for big data.
Neurocomputing, 2016, 195: 143-148.
Zhang S, Li Y, Cheng D, et al.
Efficient subspace clustering based on self-representation and grouping effect.
Neural Computing and Applications, 2018, 29(1): 51-59.
Zhu X, Zhang S, Hu R, et al.
Local and global structure preservation for robust unsupervised spectral feature selection.
IEEE Transactions on Knowledge and Data Engineering, 2017, 30(3): 517-529.
Deng Z, Zhang S, Yang L, et al
Sparse sample self-representation for subspace clustering.
Neural Computing and Applications, 2018, 29(1): 43-49.
Zhu Y, Zhang X, Wang R, et al.
Self-representation and PCA embedding for unsupervised feature selection.
World Wide Web, 2018, 21(6): 1675-1688.
Wei Zheng, Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang.
Robust Feature Selection on Incomplete Data.
IJCAI 2018: 3191-3197.
Xiaofeng Zhu, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, Shichao Zhang.
Robust Graph Dimensionality Reduction.
IJCAI 2018: 3257-3263
甘江璋,钟智,余浩,雷聪,赵树之.
基于自步学习多元回归分析.
计算机工程与设计,2018,39(12):3835-3839+3852.
雷聪，钟智，胡晓依，方月，余浩，郑威.
基于超图的稀疏属性选择算法.
计算机应用研究,2018,35(11):3213-3216+3219.
苏毅娟,余浩,雷聪,郑威,李永钢.
基于PCA的哈希图像检索算法.
计算机应用研究,2018,35(10):3147-3150.
郑威,文国秋,何威,胡荣耀,赵树之.
属性自表达的低秩无监督属性选择算法.
广西师范大学学报(自然科学版),2018,36(01):61-69.
罗䶮,苏毅娟,雷聪,胡荣耀,杨利锋,李永钢.
基于超图稀疏的低秩属性选择算法用于多回归分析.
计算机应用研究,2018,35(09):2671-2675.
钟智,方月,胡荣耀,李永钢,雷聪.
基于超图表示的低秩属性选择方法用于回归分析.
计算机应用研究,2018,35(07):2046-2050.
Zhu X, Zhang S, Hu R, et al.
Local and global structure preservation for robust unsupervised spectral feature selection.
IEEE Transactions on Knowledge and Data Engineering, 2017, 30(3): 517-529.
Zhang S, Li X, Zong M, et al.
Efficient knn classification with different numbers of nearest neighbors.
IEEE transactions on neural networks and learning systems, 2017, 29(5): 1774-1785.
Zhang S, Li X, Zong M, et al.
Learning k for knn classification.
ACM Transactions on Intelligent Systems and Technology (TIST), 2017, 8(3): 43.
Xiaofeng Zhu, Zhi Jin, Rongrong Ji.
Learning high-dimensional multimedia data Multimedia Systems.
Multimedia Systems (2017) 23:281–283.
Zheng W, Zhu X, Zhu Y, et al.
Dynamic graph learning for spectral feature selection.
Multimedia Tools and Applications, 2017.
He W, Zhu X, Cheng D, et al.
Unsupervised feature selection for visual classification via feature-representation property.
Neurocomputing, 2017, 236: 5-13.
He W, Cheng X, Hu R, et al.
Feature self-representation based hypergraph unsupervised feature selection via low-rank representation.
Neurocomputing, 2017, 253: 127-134.
Hu R, Cao J, Cheng D, et al.
Self-representation dimensionality reduction for multi-model classification.
Neurocomputing, 2017, 253: 154-161.
Cheng X, Zhu Y, Song J, et al.
A novel low-rank hypergraph feature selection for multi-view classification.
Neurocomputing, 2017, 253: 115-121.
Zhu Y, Zhang X, Wen G, et al.
Double sparse-representation feature selection algorithm for classification.
Multimedia Tools and Applications, 2017, 76(16): 17525-17539.
Li Y, Zhang S, Cheng D, et al.
Spectral clustering based on hypergraph and self-re-presentation.
Multimedia Tools and Applications, 2017, 76(16): 17559-17576.
Hu R, Cheng D, He W, et al.
Low-rank feature selection for multi-view regression.
Multimedia Tools and Applications, 2017, 76(16): 17479-17495.
Zhang S, Yang L, Deng Z, et al.
Leverage triple relational structures via low-rank feature reduction for multi-output regression.
Multimedia Tools and Applications, 2017, 76(16): 17461-17477.
He W, Zhu X, Cheng D, et al.
Low-rank unsupervised graph feature selection via feature self-representation.
Multimedia Tools and Applications, 2017, 76(9): 12149-12164.
Cheng D, Zhang S, Liu X, et al.
Feature selection by combining subspace learning with sparse representation.
Multimedia Systems, 2017, 23(3): 285-291.
Zhu Y, Liang Z, Liu X, et al.
Self-representation graph feature selection method for classification.
Multimedia Systems, 2017, 23(3): 351-356.
Hu R, Zhu X, Cheng D, et al.
Graph self-representation method for unsupervised feature selection.
Neurocomputing, 2017, 220: 130-137.
Zhu Y, Liang Z, Liu X, et al.
Self-representation graph feature selection method for classification.
Multimedia Systems, 2017, 23(3): 351-356.
Cheng D, Zhang S, Liu X, et al.
Feature selection by combining subspace learning with sparse representation.
Multimedia Systems, 2017, 23(3): 285-291.
Zhu X, He W, Li Y, et al.
One-step spectral clustering via dynamically learning affinity matrix and subspace
Thirty-First AAAI Conference on Artificial Intelligence. 2017.
Fang Y, Zhang J, Zhang S, et al.
Supervised feature selection algorithm based on low-rank and manifold learning.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2017: 273-286.
Zhu X, Zhang S, Hu R, et al.
Local and global structure preservation for robust unsupervised spectral feature selection.
IEEE Transactions on Knowledge and Data Engineering, 2017, 30(3): 517-529.
Zhang S, Lei C, Fang Y, et al.
Unsupervised feature selection via local structure learning and self-representation.
2017 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2017: 297-302.
Zhang S, Fang Y, Lei C, et al.
Unsupervised Spectral Feature Selection with local structure learning.
2017 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2017: 303-308.
杨利锋，林大华，邓振云，李永钢.
低秩特征选择多输出回归算法.
计算机工程与应用,2017,53(20):116-121.
胡荣耀,刘星毅,程德波,何威,罗噭.
鲁棒自表达的低秩属性选择算法.
计算机工程,2017,43(09):43-50.
苏毅娟，李永钢，杨利锋，孙可，罗䶮.
基于自表征和群组效应的子空间聚类算法
计算机工程与设计,2017,38(02):534-538
苏毅娟,雷聪,胡荣耀,何威,朱永华.
基于属性自表达的低秩超图属性选择算法.
计算机应用研究,2017,34(08):2294-2298.
李永钢，苏毅娟，何威，雷聪.
基于超图和样本自表征的谱聚类算法.
计算机应用研究,2017,34(06):1621-1625.
朱永华,程德波,何威,文国秋,梁正友.
基于图的特征选择算法在阿兹海默症诊断问题研究.
计算机应用研究,2017,34(04):1018-1021.
胡荣耀,刘星毅,程德波,何威.
基于稀疏学习的低秩属性选择算法.
计算机工程与应用,2017,53(10):132-138.
Xiaofeng Zhu, Feng Lu, Chen Xu, Rongrong Ji
Learning for medical imaging.
Neurocomputing, 2016, 195(C): 1-5.
Zhu X, Li X, Zhang S, et al.
Robust joint graph sparse coding for unsupervised spectral feature selection.
IEEE transactions on neural networks and learning systems, 2016, 28(6): 1263-1275.
Zhang S, Li Y, Cheng D B, et al.
Hypergraph Spectral Clustering via Sample Self-Representation.
FSDM. 2016: 334-340.
Hu R, Zhu X, He W, et al.
Supervised Feature Selection by Robust Sparse Reduced-Rank Regression.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2016: 700-713.
He W, Zhu X, Li Y, et al.
Unsupervised Hypergraph Feature Selection with Low-Rank and Self-Representation Constraints.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2016: 172-187.
Zhang S, Yang L, Li Y, et al.
Low-Rank Feature Reduction and Sample Selection for Multi-output Regression.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2016: 126-141.
Shichao Zhang, Cong Lei, Xiaofeng Zhu.
Hypergraph-based Sparse Feature Selection via Low Matrix Rank Constraint.
CCF Big Data 2016.
Shichao Zhang, Debo Cheng, Xiaofeng Zhu, Rongyao Hu, Zhenyun Deng.
Discriminative Feature Selection algorithm for Supervised Learning.
CCF Big Data 2016.
何威,刘星毅,程德波,胡荣耀.
基于稀疏学习的鲁棒自表达属性选择算法.
计算机应用与软件,2016,33(11):193-196+239.
钟智,何威,程德波,胡荣耀,刘星毅.
基于子空间学习的图稀疏属性选择算法.
计算机应用研究,2016,33(09):2679-2682.
宗鸣,龚永红,文国秋,程德波,朱永华.
基于稀疏学习的kNN分类.
广西师范大学学报(自然科学版),2016,34(03):39-45
钟智，胡荣耀，何威，文国秋，梁正友.
基于图稀疏的自表达属性选择算法.
计算机工程与设计,2016,37(06):1643-1648.
苏毅娟,邓振云,程德波,宗鸣.
大数据下的快速KNN分类算法.
计算机应用研究,2016,33(04):1003-1006+1023.
邓振云,龚永红,孙可,张继连.
基于局部相关性的kNN分类算法.
广西师范大学学报(自然科学版),2016,34(01):52-58
苏毅娟, 孙可, 邓振云, et al.
基于LPP和l2,1的KNN填充算法.
广西师范大学学报：自然科学版, 2015(4):55-62.
龚永红,宗鸣,朱永华,程德波.
基于混合模重构的kNN回归.
计算机应用与软件,2016,33(02):232-236+241.
Zhu X, Xie Q, Zhu Y, et al.
Multi-view multi-sparsity kernel reconstruction for multi-class image classification.
Neurocomputing, 2015, 169: 43-49.
Zhang S, Zong M, Sun K, et al.
Efficient kNN algorithm based on graph sparse reconstruction.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014: 356-369.
Cheng D, Zhang S, Deng Z, et al.
kNN algorithm with data-driven k value.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014: 499-512.
Yan J, Cheng D, Zong M, et al.
Improved spectral clustering algorithm based on similarity measure.
International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014: 641-654.
龚永红,邓振云,孙可,刘越.
基于LPP和Lasso的kNN回归算法.
小型微型计算机系统,2015,36(11):2604-2608.
孙可,龚永红,邓振云.
一种高效的K值自适应的SA-KNN算法.
计算机工程与科学,2015,37(10):1965-1970.
程德波，苏毅娟，宗鸣，朱永华.
基于稀疏学习的自适应近邻分类算法.
计算机工程与设计,2015,36(07):1912-1916.
苏毅娟,程德波,宗鸣,李凌,朱永华.
稀疏编码的最近邻填充算法.
计算机应用研究,2015,32(07):1942-1945.
