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A feature extraction model based on discriminative graph signals

机译：基于判别图信号的特征提取模型

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Classification finds wide applications in artificial intelligence and expert systems. Feature extraction is a key step for classifier learning. However, the relation among samples is usually ignored in classical feature extraction models. Recently, feature extraction based on graph signal processing that makes use of the relation among samples has attracted great attention. It is a common assumption that the classification information is smooth and of low frequency in these studies. We point out that it is the discrimination ability that essentially makes a good classification feature instead of smoothness. This new perspective prompts us to introduce the concept of discriminative graph signal, and then, based on this concept, we propose a novel feature extraction model for supervised classification. To improve the classification ability for multi-class problems, a generalized model is proposed to extract multiple discriminative signals and an algorithm is also presented to compute the multiple discriminative signals simultaneously. On five publicly available UCI datasets, our proposed method outperforms the existing methods in terms of performance. Finally some drawbacks are discussed and future research directions are also provided. (C) 2019 Elsevier Ltd. All rights reserved.
机译：分类在人工智能和专家系统中有广泛的应用。特征提取是分类器学习的关键步骤。但是，样本之间的关系在经典特征提取模型中通常被忽略。近来，基于利用样本之间的关系的图形信号处理的特征提取引起了极大的关注。在这些研究中，通常认为分类信息是平滑且低频的。我们指出，辨别能力本质上是一种良好的分类特征，而不是平滑度。这个新的观点促使我们引入了判别式图形信号的概念，然后，基于该概念，我们提出了一种用于监督分类的新颖特征提取模型。为了提高多类问题的分类能力，提出了一种提取多个判别信号的通用模型，并提出了一种同时计算多个判别信号的算法。在五个公开可用的UCI数据集上，我们提出的方法在性能方面优于现有方法。最后讨论了一些缺点，并提供了未来的研究方向。 （C）2019 Elsevier Ltd.保留所有权利。

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