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Sharp kernel clustering algorithms and their associated Grothendieck inequalities: Extended abstract
[chapter]
2010
Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms
In the kernel clustering problem we are given a (large) n × n symmetric positive semidefinite matrix A = (a ij ) with We design a polynomial time approximation algorithm that achieves an approximation ratio of R(B) 2 C(B) , where R(B) and C(B) are geometric parameters that depend only on the matrix B, defined as follows: if bij = vi, vj is the Gram matrix representation of B for some v1, . . . , v k ∈ R k then R(B) is the minimum radius of a Euclidean ball containing the points {v 1 , . . . , v
doi:10.1137/1.9781611973075.55
dblp:conf/soda/KhotN10
fatcat:jr7din5bknaqfe46mownnliv4e