1 |
VIDAL R. Subspace clustering[J]. IEEE Signal Processing Magazine, 2011, 28(2):52-68. 10.1109/msp.2010.939739
|
2 |
VIDAL R, MA Y, SASTRY S. Generalized Principal Component Analysis (GPCA)[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(12):1945-1959. 10.1109/tpami.2005.244
|
3 |
COSTEIRA J P, KANADE T . et al. A multibody factorization method for independently moving objects[J]. International Journal of Computer Vision, 1998, 29(3):159-179. 10.1023/a:1008000628999
|
4 |
CHEN G L, LERMAN G. Spectral Curvature Clustering (SCC)[J]. International Journal of Computer Vision, 2009, 81(3):317-330. 10.1007/s11263-008-0178-9
|
5 |
LU C Y, MIN H, ZHAO Z Q, et al. Robust and efficient subspace segmentation via least squares regression[C]// Proceedings of the 2012 European Conference on Computer Vision, LNCS 7578. Berlin: Springer, 2012:347-360.
|
6 |
McWILLIAMS B, MONTANA G. Subspace clustering of high-dimensional data: a predictive approach[J]. Data Mining and Knowledge Discovery, 2014, 28(3):736-772. 10.1007/s10618-013-0317-y
|
7 |
MA Y, YANG A Y, DERKSEN H, et al. Estimation of Subspace arrangements with applications in modeling and segmenting mixed data[J]. SIAM review, 2008, 50(3): 413-458. 10.1137/060655523
|
8 |
ARCHAMBEAU C, DELANNAY N, VERLEYSEN M. Mixtures of robust probabilistic principal component analyzers[J]. Neurocomputing, 2008, 71(7/8/9):1274-1282. 10.1016/j.neucom.2007.11.029
|
9 |
TSENG P. Nearest q-flat to m points[J]. Journal of Optimization Theory and Applications, 2000, 105(1):249-252. 10.1023/a:1004678431677
|
10 |
ZHOU W M, LIU H, XU Q P, et al. Glycerol’s generalized two-dimensional correlation IR/NIR spectroscopy and its principal component analysis[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 228:No.117824. 10.1016/j.saa.2019.117824
|
11 |
ELHAMIFAR E, VIDAL R. Sparse subspace clustering[C]// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009:2790-2797. 10.1109/cvpr.2009.5206547
|
12 |
LIU G C, LIN S C, YAN S C, et al. Robust recovery of subspace structures by low-rank representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1):171-184. 10.1109/tpami.2012.88
|
13 |
WANG Y X, XU H, LENG C L. Provable subspace clustering: when LRR meets SSC[J]. IEEE Transactions on Information Theory, 2019, 65(9):5406-5432. 10.1109/tit.2019.2915593
|
14 |
LI C G, VIDAL R. Structured sparse subspace clustering: a unified optimization framework[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015:277-286. 10.1109/cvpr.2015.7298624
|
15 |
PENG X, ZHANG L, YI Z. Scalable sparse subspace clustering[C]// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2013:430-437. 10.1109/cvpr.2013.62
|
16 |
PATEL V M, VIDAL R. Kernel sparse subspace clustering[C]// Proceedings of the 2014 IEEE International Conference on Image Processing. Piscataway: IEEE, 2014:2849-2853. 10.1109/icip.2014.7025576
|
17 |
SOLTANOLKOTABI M, ELHAMIFAR E, CANDES E J. Robust subspace clustering[J]. Annals of Statistics, 2014, 42(2):669-699. 10.1214/13-aos1199
|
18 |
XU J, XU K, KE C, et al. Reweighted sparse subspace clustering[J]. Computer Vision and Image Understanding, 2015, 138:25-37. 10.1016/j.cviu.2015.04.003
|
19 |
YOU C, ROBINSON D P, VIDAL R. Scalable sparse subspace clustering by orthogonal matching pursuit[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016:3918-3927. 10.1109/cvpr.2016.425
|
20 |
ZHANG S C, LI Y G, CHENG D B, et al. Efficient subspace clustering based on self-representation and grouping effect[J]. Neural Computing and Applications, 2018, 29(1):51-59. 10.1007/s00521-016-2353-1
|
21 |
XU G, YANG M, WU Q F. Sparse subspace clustering with low-rank transformation[J]. Neural Computing and Applications, 2019, 31(7):3141-3154. 10.1007/s00521-017-3259-2
|
22 |
CHEN Y, LI C G, YOU C. Stochastic sparse subspace clustering[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020:4154-4163. 10.1109/cvpr42600.2020.00421
|
23 |
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. 10.1109/tpami.2013.57
|
24 |
SOLTANOLKOTABI M, CANDÉS E J. A geometric analysis of subspace clustering with outliers[J]. Annals of Statistics, 2012, 40(4):2195-2238. 10.1214/12-aos1034
|
25 |
YOU C, VIDAL R. Geometric conditions for subspace-sparse recovery[C]// Proceedings of 32nd International Conference on Machine Learning. New York: JMLR.org, 2015:1585-1593. 10.1109/cvpr.2016.425
|
26 |
BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011, 3(1):1-122.
|
27 |
刘紫涵,吴鹏海,吴艳兰. 三种谱聚类算法及其应用研究[J]. 计算机应用研究, 2017, 34(4):1026-1031. 10.3969/j.issn.1001-3695.2017.04.016
|
|
LIU Z H, WU P H, WU Y L. Research of three spectral clustering algorithms and its application[J]. Application Research of Computers, 2017, 34(4):1026-1031. 10.3969/j.issn.1001-3695.2017.04.016
|
28 |
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15:1929-1958.
|
29 |
WAN L, ZEILER M, ZHANG S X, et al. Regularization of neural networks using DropConnect[C]// Proceedings of 30th International Conference on Machine Learning. New York: JMLR.org, 2014:1058-1066.
|
30 |
WAGER S, WANG S D, LIANG P. Dropout training as adaptive regularization[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2013:351-359.
|
31 |
BALDI P, SADOWSKI P. Understanding dropout[M]// BURGES C J C, BOTTOU L, WELLING M, et al. Advances in Neural Information Processing Systems 26. La Jolla, CA: Neural Information Processing Systems Foundation, 2013:2814-2822.
|
32 |
GAL Y, GHAHRAMANI Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning[C]// Proceedings of the 33rd International Conference on Machine Learning. New York: JMLR.org, 2016:1050-1059.
|
33 |
WATSON G A. Characterization of the subdifferential of some matrix norms[J]. Linear Algebra and its Applications, 1992, 170:33-45. 10.1016/0024-3795(92)90407-2
|