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CLC number: TP391

On-line Access: 2014-04-10

Received: 2013-09-06

Revision Accepted: 2014-02-08

Crosschecked: 2014-03-17

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.4 P.254-264

10.1631/jzus.C1300248


Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem


Author(s):  Sheng-kai Yang, Jian-yi Meng, Hai-bin Shen

Affiliation(s):  Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   skyangzju@gmail.com, mengjy@vlsi.zju.edu.cn, shb@vlsi.zju.edu.cn

Key Words:  Kernel method, Pre-image problem, Nonlinear denoising, Kernel PCA, Local linearity preserving


Sheng-kai Yang, Jian-yi Meng, Hai-bin Shen. Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem[J]. Journal of Zhejiang University Science C, 2014, 15(4): 254-264.

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pages="254-264",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300248"
}

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%A Jian-yi Meng
%A Hai-bin Shen
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T1 - Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem
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DOI - 10.1631/jzus.C1300248


Abstract: 
An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis (PCA) show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.

基于邻域子空间缩放维持局部线性的原像算法

研究目的:原像问题广泛存在于各种核方法中,是一个悬而未决的重要问题。由于该问题是不适定的,其解往往不唯一甚至不存在。本文对目前主流的原像求解算法进行了分析和归纳,并探讨求解原像的更有效方法。
创新要点:基于局部线性保持的基本思想,结合邻域子空间的缩放变换,提出了一种新的逆映射构造方法。
研究方法:该算法包含两个步骤:(1)通过近邻子空间缩放变换以及局部线性保持,建立训练样本的局部逆映射过程;(2)基于该局部逆映射,利用光滑性假设得到测试样本的局部逆映射,进而求解得到原像。
重要结论:实验结果表明,本文提出的算法在图像降噪性能上优于目前的主流原像算法,同时具有较小的时间复杂度。

关键词:核方法;原像问题;非线性降噪;核主成分分析;局部线性保持

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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