Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition
Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition
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DOI：
10.1109/cvpr.2003.1211374
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发表时间：
2003-06
期刊：
影响因子：
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通讯作者：
G. Guo;C. Dyer
中科院分区：
文献类型：
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作者：
G. Guo;C. Dyer
引入了一种线性规划技术，该技术联合执行特征选择和分类器训练，使得特征的子集与分类器一起被最佳地选择。由于计算机视觉中的传统分类方法使用两步方法：特征选择，然后是分类器训练，因此特征选择通常是临时使用的，或者需要耗时的向前和向后搜索过程。此外，当这两个步骤分开时，难以确定使用哪些特征以及使用多少特征。本文中使用的线性规划技术，我们称之为通过线性规划的特征选择（FSLP），可以根据最近的优化结果确定特征的数量以及在所得分类函数中使用哪些特征。我们分析了为什么FSLP可以避免基于边缘分析的维数灾难问题。作为一个演示的性能，这种FSLP技术的计算机视觉任务，我们将其应用到人脸表情识别的问题。识别精度与使用支持向量机，AdaBoost算法和贝叶斯分类器的结果进行比较。
A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the classifier. Because traditional classification methods in computer vision have used a two-step approach: feature selection followed by classifier training, feature selection has often been ad hoc using heuristics or requiring a time-consuming forward and backward search process. Moreover, it is difficult to determine which features to use and how many features to use when these two steps are separated. The linear programming technique used in this paper, which we call feature selection via linear programming (FSLP), can determine the number of features and which features to use in the resulting classification function based on recent results in optimization. We analyze why FSLP can avoid the curse of dimensionality problem based on margin analysis. As one demonstration of the performance of this FSLP technique for computer vision tasks, we apply it to the problem of face expression recognition. Recognition accuracy is compared with results using support vector machines, the AdaBoost algorithm, and a Bayes classifier.