Jun 14, 2012 · In this paper we improve upon the work of Kumar and Kannan along several axes. First, we weaken the center separation bound by a factor of \sqrt{k}, and ...
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In this paper we improve upon the work of Kumar and Kannan [1] along several axes. First, we weaken the center separation bound by a factor of , and secondly ...
Jun 15, 2012 · First, we weaken the center separation bound by a factor of √k, and secondly we weaken the proximity condition by a factor of k (in other words, ...
This work gives a scalable stochastic incremental algorithm based on proximal iterations to solve the SON problem with convergence guarantees and shows that ...
One of the main technical steps here would be to bound the spectral norm of a random n×d matrix Y whose rows are chosen independently. We use the following ...
Missing: Improved | Show results with:Improved
Sep 8, 2024 · Concerning the Lp norm of spectral clusters for second-order elliptic operators on compact manifolds. Article. Full-text available. Mar 1988.
Specifically, we obtain bounds on the eigenvectors of graph Laplacian matrices in terms of the between cluster separation, and within cluster connectivity.
Improved spectral-norm bounds for clustering. P Awasthi, O Sheffet. International Workshop on Approximation Algorithms for Combinatorial …, 2012. 133, 2012.
Abstract. The two-step spectral clustering method, which consists of the Laplacian eigenmap and a rounding. 4 step, is a widely used method for graph ...
In multivariate statistics, spectral clustering techniques make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform ...