IEEE Trans Neural Netw Learn Syst - Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ imag(1947) propos(1133) code(1026) }
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{ can(981) present(881) function(850) }
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{ result(1111) use(1088) new(759) }
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Resumo

Nonparametric kernel learning (NPKL) is a flexible approach to learn the kernel matrix directly without assuming any parametric form. It can be naturally formulated as a semidefinite program (SDP), which, however, is not very scalable. To address this problem, we propose the combined use of low-rank approximation and block coordinate descent (BCD). Low-rank approximation avoids the expensive positive semidefinite constraint in the SDP by replacing the kernel matrix variable with V(T) V , where V is a low-rank matrix. The resultant nonlinear optimization problem is then solved by BCD, which optimizes each column of V sequentially. It can be shown that the proposed algorithm has nice convergence properties and low computational complexities. Experiments on a number of real-world data sets show that the proposed algorithm outperforms state-of-the-art NPKL solvers.

Resumo Limpo

nonparametr kernel learn npkl flexibl approach learn kernel matrix direct without assum parametr form can natur formul semidefinit program sdp howev scalabl address problem propos combin use lowrank approxim block coordin descent bcd lowrank approxim avoid expens posit semidefinit constraint sdp replac kernel matrix variabl vt v v lowrank matrix result nonlinear optim problem solv bcd optim column v sequenti can shown propos algorithm nice converg properti low comput complex experi number realworld data set show propos algorithm outperform stateoftheart npkl solver

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