7 search results - page 1 / 2 » Approximating Spectral Densities of Large Matrices |
JMLR
2010
12 years 11 months ago
2010
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
CPHYSICS
2011
12 years 11 months ago
2011
We present an acceleration of the well-established Krylov-Ritz methods to compute the sign function of large complex matrices, as needed in lattice QCD simulations involving the o...
NIPS
2004
13 years 5 months ago
2004
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
AAAI
2007
13 years 6 months ago
2007
A new spectral approach to value function approximation has recently been proposed to automatically construct basis functions from samples. Global basis functions called proto-val...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two re...