Jan 29, 2019 · The key to our approach is a new parallel execution schedule that allows us to perform projections at multiple metric constraints simultaneously ...
Abstract. Many clustering applications in machine learning and data mining rely on solving metric-constrained optimization prob-.
Many clustering applications in machine learning and data mining rely onsolving metric-constrained optimization problems. These problems arecharacterized by ...
Jun 5, 2018 · Abstract:We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables.
Missing: Parallel | Show results with:Parallel
It is proved that the metric-constrained linear program relaxation of correlation clustering is equivalent to a special case of the metric nearness problem, ...
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We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables. We refer to this as metric-constrained ...
We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables. We refer to this as metric-constrained ...
An algorithm is proposed for the minimization of a smooth function subject to smooth inequality constraints. Unlike sequential quadratic programming type ...
We address the problem of merging graph and feature-space information while learning a metric from structured data. Existing algorithms tackle the problem ...
In this paper, we consider a modification of the parallel projection method for solving overdetermined nonlinear systems of equations introduced recently by ...
Missing: Metric | Show results with:Metric