In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels capturing multi-feature associations. Then, based on Riemannian geometry, a composite kernel is computed, extracting the differences between the learned feature associations. Finally, a FS score based on spectral analysis is proposed. Considering multi-feature associations makes our method multivariate by design. This in turn allows for the extraction of the hidden manifold underlying the features and avoids overfitting, facilitating few-sample FS. We showcase the efficacy of our method on illustrative examples and several benchmarks, where our method demonstrates higher accuracy in selecting the informative features compared to competing methods. In addition, we show that our FS leads to improved classification and better generalization when applied to test data.
翻译:在本文中,我们为少数抽样监督特性选择提供了一种新方法。 我们的方法首先利用捕捉多功能协会的内核来学习每个类的特色空间的方方面面。 然后,根据里曼语的几何学,计算出一个复合内核,从学过特性协会之间得出差异。 最后,我们根据光谱分析提出一个FS评分。考虑到多功能协会,我们的方法因设计而具有多变性。这反过来又允许提取特征背后的隐藏的方块,避免过度装配,便利少数抽样FS。我们展示了我们的方法在示例和几个基准方面的功效,我们的方法在选择信息特性时与竞争方法相比更加准确。此外,我们还表明我们的FS在测试数据时可以改进分类和更加普及。