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Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces.
“Gradient-based learning applied to document recognition,”
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, · 1998
Earlier work this paper cites.
“Lambertian reflectance and linear subspaces,”
R. Basri and D. W. Jacobs, · 2001
Earlier work this paper cites.
“From few to many: Illumination cone models for face recognition under variable lighting and pose,”
A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, · 2001
Earlier work this paper cites.
“Acquiring linear subspaces for face recognition under variable lighting,”
D. J. Kriegman K.C. Lee, J. Ho, · 2005
Earlier work this paper cites.
“Reducing the dimensionality of data with neural networks,”
G. E. Hinton and R. R. Salakhutdinov., · 2006
Earlier work this paper cites.
“Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories,”
S. R. Rao, R. Tron, R. Vidal, and Y. Ma, · 2010
Earlier work this paper cites.
“Robust subspace segmentation by low-rank representation,”
G. Liu, Z. Lin, and Y. Yu, · 2010
Earlier work this paper cites.
“Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance,”
N. X. Vinh, J. Epps, and J. Bailey, · 2010
Earlier work this paper cites.
“Introduction to information retrieval,”
P. Raghavan C. Manning and H. Schutze, · 2010
Earlier work this paper cites.
“Sparse subspace clustering: Algorithm, theory, and applications,”
E. Elhamifar and R. Vidal, · 2012
Cited alongside, same era.
“Robust and efficient subspace segmentation via least squares regression,”
C.Y. Lu, H. Min, Z.Q. Zhao, L. Zhu, D.S. Huang, and S. Yan, · 2012
Cited alongside, same era.
“Imagenet classification with deep convolutional neural networks,”
A. Krizhevsky, I. Sutskever, and G. E. Hinton, · 2012
Cited alongside, same era.
“Latent space sparse subspace clustering,”
V. M. Patel, H. V. Nguyen, and R. Vidal, · 2013
Cited alongside, same era.
“Low rank subspace clustering (LRSC),”
R. Vidal and P. Favaro, · 2014
Cited alongside, same era.
“Kernel sparse subspace clustering,”
V. M. Patel and R. Vidal, · 2014
Cited alongside, same era.
“Scalable sparse subspace clustering by orthogonal matching pursuit,”
C. You, D. P. Robinson, and R. Vidal, · 2016
Later among the works it cites.
“Robust kernel low-rank representation,”
S. Xiao, M. Tan, D. Xu, and Zhao Y. D., · 2016
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“Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,”
X.J. Mao, C. Shen, and Y.B. Yang, · 2016
Later among the works it cites.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2016
Later among the works it cites.
“Tensorflow: Large-scale machine learning on heterogeneous distributed systems,”
M. Abadi, A. Agarwal, and P. Barham, · 2016
Later among the works it cites.
“Deep subspace clustering networks,”
P. Ji, T. Zhang, H. Li, M. Salzmann, and I. D. Reid, · 2017
Later among the works it cites.
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“Training very deep networks,”
R. K. Srivastava, K. Greff, and J. Schmidhuber, · 2015
Cited alongside, same era.
“Adam: A method for stochastic optimization,”
D. P. Kingma and J. Ba, · 2015
Cited alongside, same era.
“Oracle based active set algorithm for scalable elastic net subspace clustering,”
C. You, C.G. Li, D. P. Robinson, and R. Vidal, · 2016
Cited alongside, same era.
“Sparse-dense subspace clustering,”
S. Yang, W. Zhu, and Y. Zhu,
Cited in the paper.
“Structured sparse subspace clustering: A joint affinity learning and subspace clustering framework,”
C.G. Li, C. You, and R. Vidal, · 2017
Later among the works it cites.
“Rednet: Residual encoder-decoder network for indoor RGB-D semantic segmentation,”
J. Jiang, L. Zheng, F. Luo, and Z. Zhang, · 2018
Later among the works it cites.
“Fred-net: Fully residual encoder-decoder network for accurate iris segmentation,”
M. Arsalan, D. Kim, M. Lee, M. Owais, and R.Kang, · 2019
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