Spectral Algorithms and Representations

1. Isotropic PCA and Affine-Invariant Clustering (S.C. Brubaker) [conf version]
   Building Bridges (Ed.s M. Gr\"{o}tchel and G.O.H.Katona), Bolyai Society Mathematical Studies, 2008. (special issue in honor of Laci Lovasz).
   Proc. of FOCS 2008.
   
2. Adaptive Sampling and Fast Low-rank Matrix Approximation (A. Deshpande)
   Proc. of RANDOM, 2006.
   
3. Matrix Approximation and Projective Clustering via Volume Sampling(A. Deshpande, L. Rademacher and G. Wang)
   Proc. of the 17th ACM-SIAM Symposium on Discrete Algorithms, 2006.
   
4. A Divide-and-Merge Methodology for Clustering (D. Cheng, R. Kannan and G. Wang)
   ACM Transactions on Database Systems, 2006.
   Proc. of the ACM Symposium on Principles of Database Systems, 2005.
   
5. The spectral method for general mixture models (R. Kannan and H. Salmasian)
   SIAM J. on Computing, 2008
   Proc. of the 18th Conference on Learning Theory, 2005 (Mark Fulk award).
   
6. Tensor decomposition and approximation schemes for constraint satisfaction problems.
   (W. F. de la Vega, R. Kannan and M. Karpinski)
   Proc. of the 37th ACM Symposium on the Theory of Computing (STOC '05), 2005.
   
7. A spectral algorithm for learning mixtures of distributions. (Grant Wang)
   Proc. of the 43rd IEEE Foundations of Computer Science (FOCS '02), Vancouver, 2002.
   JCSS (special issue for FOCS '02), 68(4), 841--860, 2004.
   
8. On clusterings: good, bad and spectral. (Ravi Kannan and Adrian Vetta)
   Journal of the ACM (JACM) 51(3), 497--515, 2004.
   Proc. of the 41st Foundations of Computer Science (FOCS '00), Redondo Beach, 2000.
   
9. Optimal outlier removal in high-dimensional spaces. (John Dunagan)
   Proc. of the 33rd ACM Symposium on the Theory of Computing (STOC '01), Crete, 2001.
   JCSS (special issue for STOC '01), 68(2), 335--373, 2004.
   
10. Clustering large graphs via the Singular Value Decomposition.
    (Petros Drineas, Ravi Kannan, Alan Frieze and V. Vinay)
    Proc. of the 10th ACM-SIAM Symposium on Discrete Algorithms, Baltimore, 1999.
    Machine Learning 56, 9--33, 2004.
    
11. Fast Monte-Carlo Algorithms for Finding Low-Rank Approximations. (A. Frieze, R. Kannan)
    Proc. of the 39th Foundations of Computer Science (FOCS '98), Palo Alto, 1998.
    Journal of the ACM (JACM), 51(6), 1025-1041, 2004.
    
12. Locality-Preserving Hashing in Multidimensional Spaces.
    (Piotr Indyk, Rajeev Motwani and Prabhakar Raghavan)
    Proc. 29th ACM Symposium on the Theory of Computing (STOC '97), El Paso, 1997.
    
13. Latent Semantic Indexing: A Probabilistic Analysis.
    (Christos Papadimitriou, Prabhakar Raghavan and Hisao Tamaki)
    Proc. 17th ACM Symposium on the Principles of Database Systems, Seattle, 1998.
    Journal of Comp. and System Sciences (special issue for PODS '01), 61, 2000 217-235.
    
14. The Colin de Verdičre Number and Sphere Representations of a Graph
    (Andrew Kotlov and László Lovász)
    Combinatorica, 17(4), 1997.