Spectral Methods

## Spectral Methods

• Asymptotic behavior of $\ell_p$-based Laplacian regularization in semi-supervised learning. A. El Alaoui, X. Cheng, A. Ramdas, M. Wainwright and M. I. Jordan. Proceedings of the Conference on Computational Learning Theory (COLT), New York, NY, 2016.

• Spectral methods meet EM: A provably optimal algorithm for crowdsourcing. Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan. Journal of Machine Learning Research, 101, 1-44, 2016.

• The constrained Laplacian rank algorithm for graph-based clustering. F. Nie, X. Wang, M. I. Jordan, H. Huang. In Proceedings of the Thirtieth Conference on Artificial Intelligence (AAAI), Phoenix, AZ, 2016.

• Distributed matrix completion and robust factorization. L. Mackey, A. Talwalkar and M. I. Jordan. Journal of Machine Learning Research, 16, 913-960, 2015.

• Revisiting k-means: New algorithms via Bayesian nonparametrics. B. Kulis and M. I. Jordan. In J. Langford and J. Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK, 2012.

• Active spectral clustering via iterative uncertainty reduction. F. Wauthier, N. Jojic, and M. I. Jordan. 18th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Beijing, China, 2012.

• Divide-and-conquer matrix factorization. L. Mackey, A. Talwalkar and M. I. Jordan. In P. Bartlett, F. Pereira, J. Shawe-Taylor and R. Zemel (Eds.) Advances in Neural Information Processing Systems (NIPS) 24, 2012.

• Fast approximate spectral clustering. D. Yan, L. Huang, and M. I. Jordan. 15th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Paris, France, 2009. [Software]. [Long version].

• Spectral clustering with perturbed data. L. Huang, D. Yan, M. I. Jordan, and N. Taft. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009. [Long version].

• Multiway spectral clustering: A maximum margin perspective. Z. Zhang and M. I. Jordan. Statistical Science, 23, 383-403, 2008.

• Spectral clustering for speech separation. F. R. Bach and M. I. Jordan. In J. Keshet and S. Bengio (Eds.), Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. New York: John Wiley, 2008.

• Regression on manifolds using kernel dimension reduction. J. Nilsson, F. Sha, and M. I. Jordan. Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.

• Learning spectral clustering, with application to speech separation. F. R. Bach, and M. I. Jordan. Journal of Machine Learning Research, 7, 1963-2001, 2006.

• Blind one-microphone speech separation: A spectral learning approach. F. R. Bach and M. I. Jordan. Advances in Neural Information Processing Systems (NIPS) 16, 2004.

• Learning spectral clustering. F. R. Bach and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.

• On semidefinite relaxation for normalized k-cut and connections to spectral clustering. E. P. Xing and M. I. Jordan. Technical Report CSD-03-1265, Division of Computer Science, University of California, Berkeley, 2003.

• On spectral clustering: Analysis and an algorithm. A. Y. Ng, M. I. Jordan, and Y. Weiss. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.