NDLI: An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection

Content Provider	Springer Nature Link
Author	Wang, Zhe Fan, Qi Jie, Wenbo Gao, Daqi
Copyright Year	2014
Abstract	Multiple empirical kernel learning (MEKL) is demonstrated to be flexible and effective due to introducing multiple kernels. But MEKL also brings a large computational complexity in practice. Therefore, in this paper we adopt the random projection (RP) technique to efficiently construct the low-dimensional feature space, and then develop an efficient and effective MEKL named MEKLRP so as to decrease the computational complexity. The proposed MEKLRP randomly selects a subset $$S'$$ of $$p$$ samples from the whole training set $$S$$ of $$N$$ samples, and then utilizes $$S'$$ to generate $$M$$ different EKMs $$\{\Phi ^{rpe}_l(x)\}_{l=1}^M$$ . Following that, MEKLRP maps each sample $$x$$ into $$\Phi _l^{rpe}(x), l=1...M$$ . Finally, MEKLRP applies the transformed samples into our previous MEKL framework. We highlight the contributions of the MEKLRP as follows. Firstly, the MEKLRP adopts the random characteristic of RP and efficiently decreases the computational cost of the matrix eigen-decomposition from $$O(N^3)$$ to $$O(p^3)$$ . Secondly, the MEKLRP maintains an approximate separability at one certain margin and preserves most of the discriminant information in a low-dimensional space since the characteristic of RP in kernel-based learning. Thirdly, the MEKLRP behaves a lower generalization risk bound than its corresponding original learning machine according to the Rademacher complexity.
Starting Page	715
Ending Page	744
Page Count	30
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	42
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-10-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multiple kernel learning Empirical mapping Random projection Rademacher complexity analysis Classifier design Pattern recognition Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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