Model-invariant viability kernel approximation

2019 ◽  
Vol 127 ◽  
pp. 13-18 ◽  
Author(s):  
Mahdi Yousefi ◽  
Klaske van Heusden ◽  
Ian M. Mitchell ◽  
Guy A. Dumont
Keyword(s):  
Viability Kernel ◽  
Kernel Approximation

scholarly journals Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

2020 ◽  
Vol 34 (04) ◽  
pp. 4844-4851
Author(s):  
Fanghui Liu ◽  
Xiaolin Huang ◽  
Yudong Chen ◽  
Jie Yang ◽  
Johan Suykens
Keyword(s):  
State Of The Art ◽  
Sampling Strategy ◽  
Prediction Performance ◽  
Current State ◽  
Kernel Approximation ◽  
Sampling Process ◽  
New Strategy ◽  
The Matrix ◽  
Benchmark Datasets ◽  
Random Fourier Features

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2+s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019).

scholarly journals Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Wenjia Niu ◽  
Kewen Xia ◽  
Baokai Zu ◽  
Jianchuan Bai
Keyword(s):  
Recognition Accuracy ◽  
Multiple Kernel Learning ◽  
Low Rank ◽  
Kernel Learning ◽  
Support Vector ◽  
Distribution Characteristics ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Kernel Approximation ◽  
Low Rank Representation

Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR). It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint. Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy. Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit. Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.

The SKN Approximation for Solving Radiative Transfer Problems in Absorbing, Emitting, and Linearly Anisotropically Scattering Plane-Parallel Medium: Part 2

2002 ◽  
Vol 124 (4) ◽  
pp. 685-695 ◽  
Author(s):  
Zekeriya Altac¸
Keyword(s):  
Radiative Transfer ◽  
Incident Energy ◽  
Radiative Heat ◽  
Isotropic Scattering ◽  
Benchmark Problems ◽  
Scattering Medium ◽  
Second Order Differential Equations ◽  
Plane Parallel ◽  
Kernel Approximation ◽  
Transfer Problems

The SKN (Synthetic Kernel) approximation is proposed for solving radiative transfer problems in linearly anisotropically scattering homogeneous and inhomogeneous participating plane-parallel medium. The radiative integral equations for the incident energy and the radiative heat flux using synthetic kernels are reduced to a set of coupled second-order differential equations for which proper boundary conditions are established. Performance of the three quadrature sets proposed for isotropic scattering medium are further tested for linearly anisotropically scattering medium. The method and its convergence with respect to the proposed quadrature sets are explored by comparing the results of benchmark problems using the exact, P11, and S128 solutions. The SKN method yields excellent results even for low orders using appropriate quadrature set.

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