A feature extraction model based on discriminative graph signals
A feature extraction model based on discriminative graph signals
复制标题
一种基于判别图信号的特征提取模型
DOI：
10.1016/j.eswa.2019.112861
复制
发表时间：
2020-01-01
影响因子：
8.5
通讯作者：
Yang, Lihua
中科院分区：
文献类型：
--
作者：
Lin, Wuhong;Huang, Jianfeng;Yang, Lihua
分类在人工智能和专家系统中有着广泛的应用。特征提取是分类器学习的关键步骤。然而，经典的特征提取模型通常忽略样本之间的关系。近年来，利用样本之间关系的基于图信号处理的特征提取引起了人们的广泛关注。这些研究中普遍的假设是分类信息是平滑的且频率较低。我们指出，本质上是区分能力而不是平滑度决定了良好的分类特征。这个新视角促使我们引入判别图信号的概念，然后基于这个概念，我们提出了一种新的用于监督分类的特征提取模型。为了提高多类问题的分类能力，提出了一种广义模型来提取多个判别信号，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出了未来的研究方向。 (C) 2019 Elsevier Ltd. 保留所有权利。
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.