/Graph-based-subspsce-clustering

Primary LanguageMATLAB

subspsce-clustering-algorithms

Subspace clustering algorithms contains：

  1. CAN: F. Nie, X. Wang, and H. Huang, “Clustering and projected clusteringwith adaptive neighbors,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2014, pp. 977–986.
  2. CLR: F. Nie, X. Wang, M. I. Jordan, and H. Huang, “The constrained laplacian rank algorithm for graph-based clustering,” in Proc. AAAI, 2016, pp. 1969–1976.
  3. LatLRR: G. Liu and S. Yan, “Latent low-rank representation for subspace segmentation and feature extraction,” in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1615-1622.
  4. LRR: G. Liu, Z. Lin, S. Yan, J. Sun, and Y. Ma, “Robust recovery of subspace structures by low-rank representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 171-184, Jan. 2013.
  5. LRSR: J. Wang, D. Shi, D. Cheng, Y. Zhang, and J. Gao, “LRSR: low-ranksparse representation for subspace clustering,” Neurocomputing, vol. 214, pp. 1026-1037, 2016.
  6. LSR: C. Lu, H. Min, Z.-Q. Zhao, L. Zhu, D.-S. Huang, and S. Yan, “Robust and efficient subspace segmentation via least squares regression,” in Proc. Eur. Conf. Comput.Vis., 2012, pp. 347-360.
  7. RSS: X. Guo, “Robust subspace segmentation by simultaneously learning data representations and their affinity matrix,” in Proc. 24th Int. Joint Conf. Artif. Intell., 2015, pp. 3547-3553.
  8. SSRSC: Xu J, Yu M, Shao L, et al, “ Scaled simplex representation for subspace clustering,” IEEE Trans. Cybern., pp. 1-13, Oct. 2019.
  9. SSC: E. Elhamifar and R. Vidal, “Sparse subspace clustering: Algorithm, theory, and applications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2765-2781, 2013.
  10. S3C: C. G. Li, C. You, and R. Vidal, “Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework,” IEEE Trans. Image Process., vol. 26, no. 6, pp. 2988- 3001, 2017.