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.