Sjöstrand Karl, Larsen Rasmus
Informatics and Mathematical Modelling, Technical University of Denmark.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):241-8. doi: 10.1007/11866565_30.
The support vector domain description is a one-class classification method that estimates the shape and extent of the distribution of a data set. This separates the data into outliers, outside the decision boundary, and inliers on the inside. The method bears close resemblance to the two-class support vector machine classifier. Recently, it was shown that the regularization path of the support vector machine is piecewise linear, and that the entire path can be computed efficiently. This paper shows that this property carries over to the support vector domain description. Using our results the solution to the one-class classification can be obtained for any amount of regularization with roughly the same computational complexity required to solve for a particularly value of the regularization parameter. The possibility of evaluating the results for any amount of regularization not only offers more accurate and reliable models, but also makes way for new applications. We illustrate the potential of the method by determining the order of inclusion in the model for a set of corpora callosa outlines.
支持向量域描述是一种单类分类方法，用于估计数据集分布的形状和范围。它将数据分为决策边界之外的异常值和边界之内的内点。该方法与两类支持向量机分类器非常相似。最近有研究表明，支持向量机的正则化路径是分段线性的，并且可以高效地计算整个路径。本文表明，这一特性也适用于支持向量域描述。利用我们的结果，对于任何正则化量，都可以以求解特定正则化参数值所需的大致相同的计算复杂度来获得单类分类的解。能够对任何正则化量评估结果，不仅能提供更准确可靠的模型，还为新应用开辟了道路。我们通过确定一组胼胝体轮廓在模型中的包含顺序来说明该方法的潜力。
