Multiple Manifolds Clustering via Local Linear Analysis

1. Duda, R. O., P. E. Hart, D. G. Stork. Pattern Classification. 2nd ed. New York, Wiley, 2000.Search in Google Scholar

2. Vida, R. Subspace Clustering. – IEEE Signal Processing Magazine, Vol. 28, 2011, No 2, pp. 52-68.10.1109/MSP.2010.939739Search in Google Scholar

3. Shi, J., J. Malik. Normalized Cuts and Image Segmentation. – IEEE Transactions Pattern Analysis Machine Intelligence, Vol. 22, 2000, No 2, pp. 888-905.10.1109/34.868688Search in Google Scholar

4. Liu, G., et al. Robust Recovery of Subspace Structures by Low-Rank Representation. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 68, 2013, No 1, pp. 171-184.10.1109/TPAMI.2012.8822487984Search in Google Scholar

5. Elhamifar, E., R. Vidal. Sparse Subspace Clustering: Algorithm, Theory, and Applications. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 2013, No 11, pp. 2765-2781.10.1109/TPAMI.2013.5724051734Search in Google Scholar

6. Wang, Y., et al. Spectral Clustering on Multiple Manifolds. – IEEE Transactions on Neural Networks, Vol. 22, 2011, No 7, pp. 1149-1161.10.1109/TNN.2011.214779821690009Search in Google Scholar

7. Cheng, B., et al. Multi-Task Low Rank Affinity Pursuit for Image Segmentation. – In: International Conference on Computer Vision, IEEE, 2011, pp. 2439-2446.10.1109/ICCV.2011.6126528Search in Google Scholar

8. Lang, C., et al. Saliency Detection by Multi Task Sparsity Pursuit. – IEEE Transactions on Image Processing, Vol. 21, 2012, No 3, pp. 1327-1338.10.1109/TIP.2011.216927421947527Search in Google Scholar

9. Bertsekas, D. P. Nonlinear Programming. Belmont, Athena Scientific, 1999.Search in Google Scholar

10. Roweis, S. T., L. K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. – Science, Vol. 290, 2000, No 5500, pp. 2323-2326.10.1126/science.290.5500.232311125150Search in Google Scholar

11. Hartigan, J. A., M. A. Wong. A k-Means Clustering Algorithm. – Journal of the Royal Statistical Society, Series C (Applied Statistics), Vol. 28, 1979, No 1, pp. 100-108.10.2307/2346830Search in Google Scholar

12. Ng, A., M. Jordan, Y. Weiss. On Spectral Clustering: Analysis and an Algorithm. – Advances in Neural Information Processing Systems, Vol. 2, 2002, pp. 849-856.Search in Google Scholar

13. Bishop, C. M. Pattern Recognition. Springer, 2006.Search in Google Scholar

14. Goldberg, A. B., et al. Multi-Manifold Semi-Supervised Learning. – AISTATS, 2009, pp. 169-176.Search in Google Scholar

15. Zhang, K., J. T. Kwok. Clustered Nyström Method for Large Scale Manifold Learning and Dimension Reduction. – IEEE Transactions on Neural Networks, Vol. 21, 2010, No 10, pp. 1576-1587.10.1109/TNN.2010.206478620805054Search in Google Scholar

16. Haro, G., G. Randall, G. Sapiro. Translated Poisson Mixture Model for Stratification Learning. – International Journal of Computer Vision, Vol. 80, 2008, No 3, pp. 358-374.10.1007/s11263-008-0144-6Search in Google Scholar

17. Zhang, Z, H. Zha. Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment. Department of Computer Science and Engineering, Pennsylvania State University, Tech. Rep. CSE-02-019, 2002.Search in Google Scholar

18. Chen, G., G. Lerman. Spectral Curvature Clustering (SCC). – International Journal of Computer Vision, Vol. 81, 2009, No 3, pp. 317-330.10.1007/s11263-008-0178-9Search in Google Scholar

19. Wang, Y., et al. Multi-Manifold Clustering. – In: Pacific Rim International Conference on Artificial Intelligence, Springer Berlin Heidelberg, 2010, pp. 280-291.10.1007/978-3-642-15246-7_27Search in Google Scholar

20. Vidal, R., Y. Ma, S. Sastry. Generalized Principal Component Analysis (GPCA). – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, 2005, No 12, pp. 1945-1959.10.1109/TPAMI.2005.24416355661Search in Google Scholar

21. Cappelli, R., D. Maltoni. Multispace KL for Pattern Representation and Classification. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, 2001, No 9, pp. 977-996.10.1109/34.955111Search in Google Scholar