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Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets

机译:监督变分相关学习,一种分析几何特征选择及其在Omic数据集中的应用

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We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.
机译:我们引入了监督变分相关学习(Suvrel),这是一种用于确定度量张量的变分方法,用于定义模式分类中基于距离的相似性,这受到了相关学习的启发。变分方法应用于代价函数,该函数惩罚较大的类内距离并有利于较小的类间距离。我们从分析上发现了使成本函数最小化的度量张量。通过使用度量张量进行线性变换对图案进行预处理,可以得到可以更有效地分类的数据集。对于某些标准分类器,我们使用公开可用的数据集测试我们的方法。在这些数据集中,有两个已通过MAQC-II项目进行了测试,即使不使用进一步的预处理,我们的结果也会改善其性能。

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