Spectral Techniques in Graph Algorithms
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
Fast computation of low-rank matrix approximations
Journal of the ACM (JACM)
Spectral clustering by recursive partitioning
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Spectral clustering with limited independence
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Random Tensors and Planted Cliques
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Foundations and Trends® in Theoretical Computer Science
Separating populations with wide data: a spectral analysis
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Spectral methods for matrices and tensors
Proceedings of the forty-second ACM symposium on Theory of computing
Boosting spectral partitioning by sampling and iteration
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Bounding the misclassification error in spectral partitioning in the planted partition model
WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
Learning mixtures of arbitrary distributions over large discrete domains
Proceedings of the 5th conference on Innovations in theoretical computer science
Hi-index | 0.00 |
In this paper, we present a new upper bound for the spectral norm of symmetric random matrices with independent (but not necessarily identical) entries. Our results improve an earlier result of Füredi and Komlós and also correct an incomplete argument in their proof.