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2010
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Learning incoherent sparse and low-rank patterns from multiple tasks

10 years 6 months ago
Learning incoherent sparse and low-rank patterns from multiple tasks
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a lowrank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained o...
Jianhui Chen, Ji Liu, Jieping Ye
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where KDD
Authors Jianhui Chen, Ji Liu, Jieping Ye
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