82 results
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**Approximation Algorithms for Tensor Clustering**
In *Algorithmic Learning Theory: 20th International Conference*, pages: 368-383, (Editors: Gavalda, R. , G. Lugosi, T. Zeugmann, S. Zilles), Springer, Berlin, Germany, ALT, October 2009 (inproceedings)

**Text Clustering with Mixture of von Mises-Fisher Distributions**
In *Text mining: classification, clustering, and applications*, pages: 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 (inbook)

**Convex Perturbations for Scalable Semidefinite Programming**
In *JMLR Workshop and Conference Proceedings Volume 5: AISTATS 2009*, pages: 296-303, (Editors: van Dyk, D. , M. Welling), MIT Press, Cambridge, MA, USA, Twelfth International Conference on Artificial Intelligence and Statistics, April 2009 (inproceedings)

**Online blind deconvolution for astronomical imaging**
In *Proceedings of the First IEEE International Conference Computational Photography (ICCP 2009)*, pages: 1-7, IEEE, Piscataway, NJ, USA, First IEEE International Conference on Computational Photography (ICCP), April 2009 (inproceedings)

**Block Iterative Algorithms for Non-negative Matrix Approximation**
In *ICDM 2008*, pages: 1037-1042, (Editors: Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu), IEEE Service Center, Piscataway, NJ, USA, Eighth IEEE International Conference on Data Mining, December 2008 (inproceedings)

**New Projected Quasi-Newton Methods with Applications**
Microsoft Research Tech-talk, December 2008 (talk)

**Block-Iterative Algorithms for
Non-Negative Matrix Approximation**
(176), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2008 (techreport)

**Approximation Algorithms for Bregman
Clustering Co-clustering and Tensor Clustering**
(177), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2008 (techreport)

**A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem**
(TR-08-28), University of Texas, Austin, TX, USA, June 2008 (techreport)

**Non-monotonic Poisson Likelihood Maximization**
(170), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2008 (techreport)

**The Metric Nearness Problem**
*SIAM Journal on Matrix Analysis and Applications*, 30(1):375-396, April 2008 (article)

**Fast Projection-based Methods for the Least Squares Nonnegative Matrix Approximation Problem**
*Statistical Analysis and Data Mining*, 1(1):38-51, February 2008 (article)

**Scalable Semidefinite Programming using Convex Perturbations**
(TR-07-47), University of Texas, Austin, TX, USA, September 2007 (techreport)

**Information-theoretic Metric Learning**
In *ICML 2007*, pages: 209-216, (Editors: Ghahramani, Z. ), ACM Press, New York, NY, USA, 24th Annual International Conference on Machine Learning, June 2007 (inproceedings)

**Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem**
In *SDM 2007*, pages: 343-354, (Editors: Apte, C. ), Society for Industrial and Applied Mathematics, Pittsburgh, PA, USA, SIAM International Conference on Data Mining, April 2007 (inproceedings)

**Modeling data using directional distributions: Part II**
(TR-07-05), University of Texas, Austin, TX, USA, February 2007 (techreport)

**A New Projected Quasi-Newton Approach for the Nonnegative Least Squares Problem**
(TR-06-54), Univ. of Texas, Austin, December 2006 (techreport)

**Information-theoretic Metric Learning**
In *NIPS 2006 Workshop on Learning to Compare Examples*, pages: 1-5, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

**Incremental Aspect Models for Mining Document Streams**
In *PKDD 2006*, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

**Efficient Large Scale Linear Programming Support Vector Machines**
In *ECML 2006*, pages: 767-774, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

**Generalized Nonnegative Matrix Approximations with Bregman Divergences**
In *Advances in neural information processing systems 18*, pages: 283-290, (Editors: Weiss, Y. , B. Schölkopf, J. Platt), MIT Press, Cambridge, MA, USA, Nineteenth Annual Conference on Neural Information Processing Systems (NIPS), May 2006 (inproceedings)

**Nonnegative Matrix Approximation: Algorithms and Applications**
Univ. of Texas, Austin, May 2006 (techreport)

**Row-Action Methods for Compressed Sensing**
In *ICASSP 2006*, pages: 868-871, IEEE Operations Center, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing, May 2006 (inproceedings)

**Clustering on the Unit Hypersphere using von Mises-Fisher Distributions**
*Journal of Machine Learning Research*, 6, pages: 1345-1382, September 2005 (article)

**Triangle Fixing Algorithms for the Metric Nearness Problem**
In *Advances in Neural Information Processing Systems 17*, pages: 361-368, (Editors: Saul, L.K. , Y. Weiss, L. Bottou), MIT Press, Cambridge, MA, USA, Eighteenth Annual Conference on Neural Information Processing Systems (NIPS), July 2005 (inproceedings)

**Generalized Nonnegative Matrix Approximations using Bregman Divergences**
Univ. of Texas at Austin, June 2005 (techreport)

**Triangle Fixing Algorithms for the Metric Nearness Problem**
Univ. of Texas at Austin, June 2004 (techreport)

**Minimum Sum-Squared Residue based clustering of Gene Expression Data**
In *SIAM Data Mining*, pages: 00-00, SDM, April 2004 (inproceedings)

**Generative Model-based Clustering of Directional Data**
In *Proc. ACK SIGKDD*, pages: 00-00, KDD, August 2003 (inproceedings)

**The Metric Nearness Problem with Applications**
Univ. of Texas at Austin, June 2003 (techreport)

**Expectation Maximization for Clustering on Hyperspheres**
Univ. of Texas at Austin, February 2003 (techreport)

**Modeling Data using Directional Distributions**
Univ. of Texas at Austin, January 2003 (techreport)