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Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

机译：使用迭代半监督支持向量机算法的联合特征重新提取和分类

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摘要

The focus of this paper is on joint feature re-extraction and classification in cases when the training data set is small. An iterative semi-supervised support vector machine (SVM) algorithm is proposed, where each iteration consists both feature re-extraction and classification, and the feature re-extraction is based on the classification results from the previous iteration. Feature extraction is first discussed in the framework of Rayleigh coefficient maximization. The effectiveness of common spatial pattern (CSP) feature, which is commonly used in Electroencephalogram (EEG) data analysis and EEG-based brain computer interfaces (BCIs), can be explained by Rayleigh coefficient maximization. Two other features are also defined using the Rayleigh coefficient. These features are effective for discriminating two classes with different means or different variances. If we extract features based on Rayleigh coefficient maximization, a large training data set with labels is required in general; otherwise, the extracted features are not reliable. Thus we present an iterative semi-supervised SVM algorithm embedded with feature re-extraction. This iterative algorithm can be used to extract these three features reliably and perform classification simultaneously in cases where the training data set is small. Each iteration is composed of two main steps: (ⅰ) the training data set is updated/augmented using unlabeled test data with their predicted labels; features are re-extracted based on the augmented training data set. (ⅱ) The re-extracted features are classified by a standard SVM. Regarding parameter setting and model selection of our algorithm, we also propose a semi-supervised learning-based method using the Rayleigh coefficient, in which both training data and test data are used. This method is suitable when cross-validation model selection may not work for small training data set. Finally, the results of data analysis are presented to demonstrate the validity of our approach.
机译：本文的重点是在训练数据集很小的情况下的联合特征重新提取和分类。提出了一种迭代半监督支持向量机（SVM）算法，其中每次迭代均包含特征重新提取和分类，特征重新提取基于先前迭代的分类结果。首先在瑞利系数最大化的框架中讨论特征提取。可以通过瑞利系数最大化来解释脑电图（EEG）数据分析和基于EEG的脑计算机接口（BCI）中常用的公共空间模式（CSP）功能的有效性。使用瑞利系数还定义了其他两个特征。这些特征对于区分具有不同均值或不同方差的两个类别有效。如果我们基于瑞利系数最大化来提取特征，则通常需要带有标签的大型训练数据集。否则，提取的特征将不可靠。因此，我们提出了一种嵌入了特征重新提取的迭代半监督SVM算法。此迭代算法可用于可靠地提取这三个特征，并在训练数据集较小的情况下同时执行分类。每个迭代包括两个主要步骤：（ⅰ）使用未标签的测试数据及其预测标签对培训数据集进行更新/增强；基于增强训练数据集重新提取特征。 （ⅱ）通过标准SVM对重新提取的特征进行分类。关于我们算法的参数设置和模型选择，我们还提出了一种使用瑞利系数的半监督学习方法，该方法同时使用了训练数据和测试数据。当交叉验证模型选择可能不适用于小型训练数据集时，此方法适用。最后，提出了数据分析的结果以证明我们方法的有效性。

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