利用 L1 准则改进二维线性判别分析,优化脑电信号

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-22 DOI:10.1016/j.ins.2024.121585
Bin Lu , Fuwang Wang , Junxiang Chen , Guilin Wen , Rongrong Fu
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引用次数: 0

摘要

降维是处理高维数据集的一个关键因素。基于 L1 准则的二维线性判别分析(L1-2DLDA)在这方面得到了广泛应用,但它对异常值和偏差较大的类仍然很敏感,从而降低了其性能。针对这一局限性,本研究提出了成对样本距离二维线性判别分析(PSD2DLDA),这是一种利用成对样本距离对 L1-2DLDA 进行建模的新方法。为了提高计算效率,本研究还引入了一种基于类均值对之间距离的简化变体--成对类均值距离二维线性判别分析(PCD2DLDA)。与以往研究不同的是,本研究利用投影子梯度法对这两种改进方法进行了优化。同时,本研究探讨了这两种改进方法的相互关系、局限性和适用性。三个数据集的对比实验结果验证了 PSD2DLDA 和 PCD2DLDA 方法的卓越性能。特别是,与 PCD2DLDA 相比,PSD2DLDA 表现出更高的鲁棒性。此外,应用这两种方法优化脑电图(EEG)信号,有效提高了运动图像神经模式的解码精度,为优化脑机接口(BCI)应用中的脑电信号处理提供了一种前景广阔的策略。
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Improving two-dimensional linear discriminant analysis with L1 norm for optimizing EEG signal
Dimensionality reduction is a critical factor in processing high-dimensional datasets. The L1 norm-based Two-Dimensional Linear Discriminant Analysis (L1-2DLDA) is widely used for this purpose, but it remains sensitive to outliers and classes with large deviations, which deteriorates its performance. To address this limitation, the present study proposed Pairwise Sample Distance Two-Dimensional Linear Discriminant Analysis (PSD2DLDA), a novel method that modeled L1-2DLDA using pair-wise sample distances. To improve computational effectiveness, this study also introduced a streamlined variant, Pairwise Class Mean Distance Two-Dimensional Linear Discriminant Analysis (PCD2DLDA), which was based on distances between class mean pairs. Different from previous studies, this study utilized the projected sub-gradient method to optimize these two improved methods. Meanwhile, this study explored the interrelationship, limitations, and applicability of these two improved methods. The comparative experimental results on three datasets validated the outstanding performance of PSD2DLDA and PCD2DLDA methods. In particular, PSD2DLDA exhibited superior robustness compared to PCD2DLDA. Furthermore, applying these two methods to optimize electroencephalogram (EEG) signals effectively enhanced the decoding accuracy of motor imagery neural patterns, which offered a promising strategy for optimizing EEG signals processing in brain-computer interface (BCI) applications.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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