Ustün B, Melssen W J, Buydens L M C
Institute for Molecules and Materials, Analytical Chemistry, Radboud University of Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands.
Anal Chim Acta. 2007 Jul 9;595(1-2):299-309. doi: 10.1016/j.aca.2007.03.023. Epub 2007 Mar 18.
This paper introduces a technique to visualise the information content of the kernel matrix and a way to interpret the ingredients of the Support Vector Regression (SVR) model. Recently, the use of Support Vector Machines (SVM) for solving classification (SVC) and regression (SVR) problems has increased substantially in the field of chemistry and chemometrics. This is mainly due to its high generalisation performance and its ability to model non-linear relationships in a unique and global manner. Modeling of non-linear relationships will be enabled by applying a kernel function. The kernel function transforms the input data, usually non-linearly related to the associated output property, into a high dimensional feature space where the non-linear relationship can be represented in a linear form. Usually, SVMs are applied as a black box technique. Hence, the model cannot be interpreted like, e.g., Partial Least Squares (PLS). For example, the PLS scores and loadings make it possible to visualise and understand the driving force behind the optimal PLS machinery. In this study, we have investigated the possibilities to visualise and interpret the SVM model. Here, we exclusively have focused on Support Vector Regression to demonstrate these visualisation and interpretation techniques. Our observations show that we are now able to turn a SVR black box model into a transparent and interpretable regression modeling technique.
本文介绍了一种可视化核矩阵信息内容的技术以及一种解释支持向量回归（SVR）模型要素的方法。最近，在化学和化学计量学领域，使用支持向量机（SVM）解决分类（SVC）和回归（SVR）问题的情况大幅增加。这主要是由于其具有较高的泛化性能，以及能够以独特且全局的方式对非线性关系进行建模。应用核函数可以实现对非线性关系的建模。核函数将通常与相关输出属性非线性相关的输入数据转换到一个高维特征空间，在这个空间中非线性关系可以以线性形式表示。通常，支持向量机被用作一种黑箱技术。因此，该模型无法像例如偏最小二乘法（PLS）那样进行解释。例如，PLS得分和载荷使得可视化和理解最优PLS机制背后的驱动力成为可能。在本研究中，我们探讨了可视化和解释支持向量机模型的可能性。在此，我们专门聚焦于支持向量回归来展示这些可视化和解释技术。我们的观察结果表明，我们现在能够将一个SVR黑箱模型转变为一种透明且可解释的回归建模技术。
