Agudo, A., Moreno-Noguer, F.: Learning shape, motion and elastic models in force space. In: International Conference on Computer Vision, pp. 756–764 (2015)
Agudo, A., Moreno-Noguer, F.: Combining local-physical and global-statistical models for sequential deformable shape from motion. Int. J. Comput. Vis. 122(2), 371–387 (2017)
MathSciNet
Article
Google Scholar
Agudo, A., Moreno-Noguer, F.: DUST: Dual union of spatio-temporal subspaces for monocular multiple object 3D reconstruction. In: Computer Vision and Pattern Recognition, pp. 1513–1521 (2017)
Agudo, A., Moreno-Noguer, F.: Force-based representation for non-rigid shape and elastic model estimation. IEEE Trans. Pattern Anal. Mach. Intell. 40(9), 2137–2150 (2018)
Article
Google Scholar
Akhter, I., Simon, T., Khan, S., Matthews, I., Sheikh, Y.: Bilinear spatiotemporal basis models. TOG 31(2), 17:1-17:12 (2012)
Article
Google Scholar
Balzano, L., Szlam, A., Recht, B., Nowak, R.: K-subspaces with missing data. In: Statistical Signal Processing Workshop, pp. 612–615 (2012)
Bhojanapalli, S., Jain, P.: Universal matrix completion. In: International Conference on Machine Learning, pp. 1881–1889 (2013)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)
Article
Google Scholar
Cabral, R., de la Torre, F., Costeira, J. P., Bernardino, A.: Unifying nuclear norm and bilinear factorization approaches for low-rank matrix decomposition. In: International Conference on Computer Vision, pp. 2488–2495 (2013)
Cai, J., Candes, E., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)
MathSciNet
Article
Google Scholar
Candès, E.J., Plan, Y.: Matrix completion with noise. IEEE J. Mag. 99(6), 925–936 (2010)
Google Scholar
Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717 (2008)
MathSciNet
Article
Google Scholar
Chen, W.Y., Song, Y., Bai, H., Lin, C., Chang, E.: Parallel spectral clustering in distributed systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 568–586 (2010)
Article
Google Scholar
Chen, Y., Xu, H., Caramanis, C., Sanghavi, S.: Robust matrix completion with corrupted columns. In: International Conference on Machine Learning, pp. 873–880 (2011)
Chiang, K.Y., Hsieh, C.J., Dhillon, I.S.: Matrix completion with noisy side information. In: Neural Information Processing Systems, pp. 3447–3455 (2015)
Dai, Y., Li, H., He, M.: A simple prior-free method for non-rigid structure from motion factorization. In: Computer Vision and Pattern Recognition, pp. 2018–2025 (2012)
Elhamifar, E.: High-rank matrix completion and clustering under self-expressive models. In: Neural Information Processing Systems, pp. 73–81 (2016)
Elhamifar, E., Vidal, R.: Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Article
Google Scholar
Eriksson, B., Balzano, L., Nowak, R.: High-rank matrix completion and subspace clustering with missing. In: International Conference on Artificial Intelligence and Statistics (2012)
Fan, J., Chow, T.: Sparse subspace clustering for data with missing entries and high-rank matrix completion. Neural Netw. 93(9), 36–44 (2017)
Article
Google Scholar
Ganti, R., Balzano, L., RWillett.: Matrix completion under monotonic single index models. In: Neural Information Processing Systems, pp. 1873–1881 (2015)
Ghahramani, Z., Hinton, G.E.: The EM algorithm for mixtures of factor analyzers. Technical Report CRG-TR-96-1, University of Toronto (1996)
Golub, G., Van Loan, C.: Matrix computations. Johns Hopkins Univ Pr (1996)
Gotardo, P.F.U., Martinez, A.M.: Computing smooth time-trajectories for camera and deformable shape in structure from motion with occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 2051–2065 (2011)
Article
Google Scholar
Hare, S., Saffari, A., Torr, P.: Struck: Structured output tracking with kernels. In: International Conference on Computer Vision, pp. 263–270 (2011)
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014)
Article
Google Scholar
Jia, X., Lu, H., Yang, M.: Visual tracking via adaptive structural local sparse appearance model. In: Computer Vision and Pattern Recognition, pp. 1822–1829 (2012)
Joo, H., Simon, T., Sheikh, Y.: Total capture: A 3D deformation model for tracking faces, hands, and bodies. In: Computer Vision and Pattern Recognition, pp. 8320–8329 (2018)
Kanatani, K.: Motion segmentation by subspace separation and model selection. In: International Conference on Computer Vision, pp. 586–591 (2001)
Knott, M., Bartholomew, D.: Latent Variables Models and Factor Analysis. Edward Arnold, London (1999)
MATH
Google Scholar
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Mathematical Programming (2010)
Liu, G., Yan, S.: Latent low-rank representation for subspace segmentation and feature extraction. In: International Conference on Computer Vision, pp. 1615–1622 (2011)
Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: International Conference on Machine Learning, pp. 663–670 (2010)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)
Article
Google Scholar
Ma, Y., Derksen, H., Hong, W., Wright, J.: Segmentation of multivariable mixed data via lossy data coding and compression. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1546–1562 (2007)
Article
Google Scholar
Ma, Y., Yang, A., Derksen, H., Fossum, R.: Estimation of subspace arrangements with applications in modeling and segmenting mixed data. SIAM Rev. 50(3), 413–458 (2008)
MathSciNet
Article
Google Scholar
Nguyen, D.M., Calderbank, R., Deligiannis, N.: Geometric matrix completion with deep conditional random fields. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3579–3593 (2020)
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Computer Vision and Pattern Recognition, pp. 724–732
Rao, S., Tron, R., Vidal, R., Ma, Y.: Motion segmentation in the presence of outlying, incomplete or corrupted trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1832–1845 (2010)
Article
Google Scholar
Rao, S., Yang, A., Sastry, S., Ma, Y.: Robust algebraic segmentation of mixed rigid-body and planar motions in two views. Int. J. Comput. Vis. 88(3), 425–446 (2010)
MathSciNet
Article
Google Scholar
Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev. 52(3), 471–501 (2010)
MathSciNet
Article
Google Scholar
Sui, Y., Zhao, X., Zhang, S., Yu, X., Zhao, S., Zhang, L.: Self-expressive tracking. Pattern Recog. 48(9), 2872–2884 (2015)
Article
Google Scholar
Sui, Y., Wang, G., Tang, Y., Zhang, L.: Tracking completion. In: European Conference on Computer Vision, pp. 194–209 (2016)
Tipping, M., Bishop, C.: Probabilistic principal component analysis. J. R. Stat. 21(3), 611–622 (1999)
MathSciNet
Article
Google Scholar
Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Comput. 11(2), 443–482 (1999)
Article
Google Scholar
Van der Aa, N.P., Luo, X., Giezeman, G.J., Tan, R.T., Veltkamp, R.C.: UMPM benchmark: a multi-person dataset with synchronized video and motion capture data for evaluation of articulated human motion and interaction. In: International Conference on Computer Vision Workshops, pp. 1264–1269 (2011)
Wang, D., Lu, H., Yang, M.: Least soft-thresold squares tracking. In: Computer Vision and Pattern Recognition, pp. 2371–2378 (2013)
Xiao, J., Chai, J., Kanade, T.: A closed-form solution to non-rigid shape and motion. Int. J. Comput. Vis. 67(2), 233–246 (2006)
Article
Google Scholar
Yan, J., Pollefeys, M.: A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In: European Conference on Computer Vision, pp. 94–106 (2006)
Yang, C., Robinson, D., Vidal, R.: Sparse subspace clustering with missing entries. In: International Conference on Machine Learning, pp. 2463–2472 (2015)
Yang, J., Yin, W., Zhang, Y., Wang, Y.: A fast algorithm for edge-preserving variational multichannel image restoration. SIAM J. Imaging Sci. 2(2), 569–592 (2009)
MathSciNet
Article
Google Scholar
Yang, J., Parikh, D., Batra, D.: Joint unsupervised learning of deep representations and image clusters. In: CVPR (2016)
Yao, Q., Kwok, J.T., Wang, T., Liu, T.: Large-scale low-rank matrix learning with nonconvex regularizers. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2628–2643 (2019)
Article
Google Scholar
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Low-rank sparse learning for robust visual tracking. In: European Conference on Computer Vision, pp. 470–484 (2012)
Zheng, Y., Tang, B., Ding, W., Zhou, H.: A neural autoregressive approach to collaborative filtering. In: ICML (2016)
Zhu, Y., Huang, D., de la Torre, F., Lucey, S.: Complex non-rigid motion 3D reconstruction by union of subspaces. In: Computer Vision and Pattern Recognition, pp. 1542–1549 (2014)