Exploring nonlinear relationships in chemical data using kernel-based methods
Exploring nonlinear relationships in chemical data using kernel-based methods
复制标题
使用基于核的方法探索化学数据中的非线性关系
DOI：
10.1016/j.chemolab.2011.02.004
复制
发表时间：
2011-05-01
影响因子：
3.9
通讯作者：
Fu, Guang-Hui
中科院分区：
文献类型：
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作者：
Cao, Dong-Sheng;Liang, Yi-Zeng;Fu, Guang-Hui
核方法，特别是支持向量机，已被进一步扩展为一类新的方法，它可以有效地解决化学中的非线性问题，通过简单的线性变换。实际上，核方法中所使用的核函数可以看作是处理化学中非线性数据的一种通用协议。本文详细讨论了核方法的基本思想和模块性，并结合一些简单的例子，使人们对核方法有一个更深入的理解。核方法的三个关键组成部分，即对偶形式，非线性映射和核函数，提供了一个一致的框架，基于核的算法。核方法的模块性允许线性算法与任何核函数联合收割机组合。因此，一些常用的化学计量学算法很容易扩展到他们的内核版本。(C)2011 Elsevier B.V.保留所有权利。
Kernel methods, in particular support vector machines, have been further extended into a new class of methods, which could effectively solve nonlinear problems in chemistry by using simple linear transformation. In fact, the kernel function used in kernel methods might be regarded as a general protocol to deal with nonlinear data in chemistry. In this paper, the basic idea and modularity of kernel methods, together with some simple examples, are discussed in detail to give an in-depth understanding for kernel methods. Three key ingredients of kernel methods, namely dual form, nonlinear mapping and kernel function, provide a consistent framework of kernel-based algorithms. The modularity of kernel methods allows linear algorithms to combine with any kernel function. Thus, some commonly used chemometric algorithms are easily extended to their kernel versions. (C) 2011 Elsevier B.V. All rights reserved.