Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.

中文翻译：

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一种使用多核支持向量机和局部缩放客观选择信息源的新方法。


计算机和传感技术的进步使得可用于开发支持健康、娱乐、制造等领域决策的系统的数据呈指数级增长。这一事实使得来自多个异构源的数据的融合成为机器学习中最有前途的研究领域之一。然而，在实际应用中，由于数据的可用性以及与处理、实施和开发时间相关的实施成本，减少源数量同时保持最佳系统性能是一项重要任务。在这项工作中，提出了一种在多模态系统中客观选择相关信息源的新方法。该方法利用多核学习（MKL）和支持向量机（SVM）分类器的能力，通过根据分类任务中的判别值分配权重来执行数据的最佳融合；当设计内核来表示每个数据源时，这些权重可以用作它们相关性的度量。此外，引入了三种在分类器预测阶段调整高斯核带宽的算法，以减少搜索最优解的计算成本；这些算法是对无监督学习中称为局部缩放的常用技术的改编。使用两个实际应用任务来评估所提出的方法：脑机接口（BCI）系统中分类任务的电极选择和用于乳腺癌检测的相关磁共振成像（MRI）序列的选择。 获得的结果表明，所提出的方法允许选择少量的信息源。