-
1
-
-
84976855597
-
Solution of the matrix equation AX + XB = C
-
Sept.
-
R. Bartels and G. Stewart. Solution of the matrix equation AX + XB = C. Communications of the ACM, 15(9):820-826, Sept. 1972.
-
(1972)
Communications of the ACM
, vol.15
, Issue.9
, pp. 820-826
-
-
Bartels, R.1
Stewart, G.2
-
2
-
-
0032154138
-
A multibody factorization method for independently moving objects
-
J. Costeira and T. Kanade. A multibody factorization method for independently moving objects. IJCV, 29(3):108-121, 1998.
-
(1998)
IJCV
, vol.29
, Issue.3
, pp. 108-121
-
-
Costeira, J.1
Kanade, T.2
-
3
-
-
70450184118
-
Sparse subspace clustering
-
E. Elhamifar and R. Vidal. Sparse subspace clustering. In CVPR, pages 2790-2797, 2009.
-
(2009)
CVPR
, pp. 2790-2797
-
-
Elhamifar, E.1
Vidal, R.2
-
4
-
-
84911375696
-
Sparse subspace clustering: Algorithm, theory, and applications
-
E. Elhamifar and R. Vidal. Sparse subspace clustering: Algorithm, theory, and applications. IEEE TPAMI, 2013.
-
(2013)
IEEE TPAMI
-
-
Elhamifar, E.1
Vidal, R.2
-
5
-
-
0019574599
-
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
-
M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381-395, 1981.
-
(1981)
Commun. ACM
, vol.24
, Issue.6
, pp. 381-395
-
-
Fischler, M.A.1
Bolles, R.C.2
-
6
-
-
0035363672
-
From few to many: Illumination cone models for face recognition under variable lighting and pose
-
A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE TPAMI, 23(6):643-660, 2001.
-
(2001)
IEEE TPAMI
, vol.23
, Issue.6
, pp. 643-660
-
-
Georghiades, A.1
Belhumeur, P.2
Kriegman, D.3
-
7
-
-
0035363672
-
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. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE TPAMI, 23(6):643-660, 2001.
-
(2001)
IEEE TPAMI
, vol.23
, Issue.6
, pp. 643-660
-
-
Georghiades, A.S.1
Belhumeur, P.N.2
Kriegman, D.J.3
-
8
-
-
85162319557
-
Trace lasso: A trace norm regularization for correlated designs
-
E. Grave, G. Obozinski, and F. Bach. Trace lasso: a trace norm regularization for correlated designs. In NIPS, pages 2187-2195, 2011.
-
(2011)
NIPS
, pp. 2187-2195
-
-
Grave, E.1
Obozinski, G.2
Bach, F.3
-
9
-
-
8644228268
-
Locality preserving projections
-
X. He and P. Niyogi. Locality preserving projections. In NIPS, 2003.
-
(2003)
NIPS
-
-
He, X.1
Niyogi, P.2
-
10
-
-
84911452493
-
Exploiting unsupervised and supervised constraints for subspace clustering
-
H. Hu, J. Feng, and J. Zhou. Exploiting unsupervised and supervised constraints for subspace clustering. CoRR, 2014.
-
(2014)
CoRR
-
-
Hu, H.1
Feng, J.2
Zhou, J.3
-
11
-
-
0028428774
-
A database for handwritten text recognition research
-
J. J. Hull. A database for handwritten text recognition research. IEEE TPAMI, 16(5):550-554, 1994.
-
(1994)
IEEE TPAMI
, vol.16
, Issue.5
, pp. 550-554
-
-
Hull, J.J.1
-
13
-
-
0002719797
-
The Hungarian method for the assignment problem
-
H. Kuhn. The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955.
-
(1955)
Naval Research Logistics Quarterly
, vol.2
, Issue.1-2
, pp. 83-97
-
-
Kuhn, H.1
-
14
-
-
0000321992
-
Explicit solutions of linear matrix equations
-
P. Lancaster. Explicit solutions of linear matrix equations. SIAM Review, 12(4):pp. 544-566, 1970.
-
(1970)
SIAM Review
, vol.12
, Issue.4
, pp. 544-566
-
-
Lancaster, P.1
-
15
-
-
84870197517
-
Robust recovery of subspace structures by low-rank representation
-
G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma. Robust recovery of subspace structures by low-rank representation. IEEE TPAMI, 35(1):171-184, 2013.
-
(2013)
IEEE TPAMI
, vol.35
, Issue.1
, pp. 171-184
-
-
Liu, G.1
Lin, Z.2
Yan, S.3
Sun, J.4
Yu, Y.5
Ma, Y.6
-
16
-
-
77956529193
-
Robust subspace segmentation by low-rank representation
-
G. Liu, Z. Lin, and Y. Yu. Robust subspace segmentation by low-rank representation. In ICML, pages 663-670, 2010.
-
(2010)
ICML
, pp. 663-670
-
-
Liu, G.1
Lin, Z.2
Yu, Y.3
-
17
-
-
84898805200
-
Correlation adaptive subspace segmentation by trace lasso
-
C. Lu, Z. Lin, and S. Yan. Correlation adaptive subspace segmentation by trace lasso. In ICCV, 2013.
-
(2013)
ICCV
-
-
Lu, C.1
Lin, Z.2
Yan, S.3
-
18
-
-
84867854316
-
Robust and effficient subspace segmentation via least squares regression
-
C. Y. Lu, H. Min, Z.-Q. Zhao, L. Zhu, D.-S. Huang, and S. Yan. Robust and effficient subspace segmentation via least squares regression. In ECCV, pages 347-360, 2012.
-
(2012)
ECCV
, pp. 347-360
-
-
Lu, C.Y.1
Min, H.2
Zhao, Z.-Q.3
Zhu, L.4
Huang, D.-S.5
Yan, S.6
-
19
-
-
80052411944
-
Multi-subspace representation and discovery
-
D. Luo, F. Nie, C. H. Q. Ding, and H. Huang. Multi-subspace representation and discovery. In ECML/PKDD, pages 405-420, 2011.
-
(2011)
ECML/PKDD
, pp. 405-420
-
-
Luo, D.1
Nie, F.2
Ding, C.H.Q.3
Huang, H.4
-
20
-
-
34548133659
-
Segmentation of multivariate mixed data via lossy data coding and compression
-
Y. Ma, H. Derksen, W. Hong, and J. Wright. Segmentation of multivariate mixed data via lossy data coding and compression. IEEE TPAMI, 29(9):1546-1562, 2007.
-
(2007)
IEEE TPAMI
, vol.29
, Issue.9
, pp. 1546-1562
-
-
Ma, Y.1
Derksen, H.2
Hong, W.3
Wright, J.4
-
21
-
-
33745821896
-
Spectral clustering for robust motion segmentation
-
J. H. Park, H. Zha, and R. Kasturi. Spectral clustering for robust motion segmentation. In ECCV (4), pages 390-401, 2004.
-
(2004)
ECCV
, Issue.4
, pp. 390-401
-
-
Park, J.H.1
Zha, H.2
Kasturi, R.3
-
22
-
-
77956034602
-
Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories
-
S. Rao, R. Tron, R. Vidal, and Y. Ma. Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories. IEEE TPAMI, 32(10):1832-1845, 2010.
-
(2010)
IEEE TPAMI
, vol.32
, Issue.10
, pp. 1832-1845
-
-
Rao, S.1
Tron, R.2
Vidal, R.3
Ma, Y.4
-
23
-
-
0034244751
-
Normalized cuts and image segmentation
-
J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE TPAMI, 22(8):888-905, 2000.
-
(2000)
IEEE TPAMI
, vol.22
, Issue.8
, pp. 888-905
-
-
Shi, J.1
Malik, J.2
-
24
-
-
0026943737
-
Shape and motion from image streams under orthography: A factorization method
-
C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: a factorization method. IJCV, 9(2):137-154, 1992.
-
(1992)
IJCV
, vol.9
, Issue.2
, pp. 137-154
-
-
Tomasi, C.1
Kanade, T.2
-
25
-
-
34948881815
-
A benchmark for the comparison of 3-d motion segmentation algorithm
-
R. Tron and R. Vidal. A benchmark for the comparison of 3-d motion segmentation algorithm. In Proc. CVPR, 2007.
-
(2007)
Proc. CVPR
-
-
Tron, R.1
Vidal, R.2
-
27
-
-
30144438432
-
Generalized principal component analysis (GPCA)
-
R. Vidal, Y. Ma, and S. Sastry. Generalized principal component analysis (GPCA). IEEE TPAMI, 27(12):1945-1959, 2005.
-
(2005)
IEEE TPAMI
, vol.27
, Issue.12
, pp. 1945-1959
-
-
Vidal, R.1
Ma, Y.2
Sastry, S.3
-
28
-
-
34548583274
-
A tutorial on spectral clustering
-
U. von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416, 2007.
-
(2007)
Statistics and Computing
, vol.17
, Issue.4
, pp. 395-416
-
-
Von Luxburg, U.1
-
29
-
-
80055057840
-
Efficient subspace segmentation via quadratic programming
-
S. Wang, X. Yuan, T. Yao, S. Yan, and J. Shen. Efficient subspace segmentation via quadratic programming. In AAAI, pages 519-524, 2011.
-
(2011)
AAAI
, pp. 519-524
-
-
Wang, S.1
Yuan, X.2
Yao, T.3
Yan, S.4
Shen, J.5
-
30
-
-
34948837349
-
A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate
-
J. Yan and M. Pollefeys. A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In Proc. ECCV, 2006.
-
(2006)
Proc. ECCV
-
-
Yan, J.1
Pollefeys, M.2
|