Topic
Multiple kernel learning
About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.
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08 Jun 2022TL;DR: In this paper , the authors study the kernel learning problems with ramp loss, a nonconvex but noise-resistant loss function, and show that the generalization bound for empirical ramp risk minimizer is similar to that of convex surrogate losses, which implies kernel learning with such loss function is not only noise resistant but also statistically consistent.
Abstract: We study the kernel learning problems with ramp loss, a nonconvex but noise-resistant loss function. In this work, we justify the validity of ramp loss under the classical kernel learning framework. In particular, we show that the generalization bound for empirical ramp risk minimizer is similar to that of convex surrogate losses, which implies kernel learning with such loss function is not only noise-resistant but, more importantly, statistically consistent. For adapting to real-time data streams, we introduce PA-ramp, a heuristic online algorithm based on the passive-aggressive framework, to solve this learning problem. Empirically, with fewer support vectors, this algorithm achieves robust empirical performances on tested noisy scenarios.
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03 Nov 2013TL;DR: Experimental results on a number of real-world data sets demonstrate that the proposed method is as accurate as directly solving the SDP, but can be one to two orders of magnitude faster.
Abstract: Side information is highly useful in the learning of a nonparametric kernel matrix. However, this often leads to an expensive semidefinite program SDP. In recent years, a number of dedicated solvers have been proposed. Though much better than off-the-shelf SDP solvers, they still cannot scale to large data sets. In this paper, we propose a novel solver based on the alternating direction method of multipliers ADMM. The key idea is to use a low-rank decomposition of the kernel matrix Z = X i¾ź Y, with the constraint that X = Y. The resultant optimization problem, though non-convex, has favorable convergence properties and can be efficiently solved without requiring eigen-decomposition in each iteration. Experimental results on a number of real-world data sets demonstrate that the proposed method is as accurate as directly solving the SDP, but can be one to two orders of magnitude faster.