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An improved locality sensitive discriminant analysis approach for feature extraction

机译：一种改进的局部敏感特征判别分析方法

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摘要

Recently, Locality Sensitive Discriminant Analysis (LSDA) has been proposed as an efficient feature extraction approach. By analyzing the local manifold structure of high-dimensional data, LSDA can obtain a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. However, because LSDA only takes the local information into consideration, it may fail to deal with the data set which contains some outliers. In order to address this limitation, a new algorithm called Improved Locality Sensitive Discriminant Analysis (ILSDA) is proposed in this paper. By integrating the intra-class scatter matrix into our algorithm, ILSDA can not only preserve the local discriminant neighborhood structure of the data, but also pull the outlier samples more close to their class centers, which makes it outperform the original LSDA and some other state of the art algorithms. Extensive experimental results on several publicly available image datasets show the feasibility and effectiveness of our proposed approach.
机译：最近，已经提出了局部敏感判别分析（LSDA）作为一种有效的特征提取方法。通过分析高维数据的局部流形结构，LSDA可以获得一个子空间，在该子空间中，具有相同标签的邻近点彼此靠近，而具有不同标签的邻近点彼此远离。但是，由于LSDA仅考虑本地信息，因此它可能无法处理包含一些异常值的数据集。为了解决这一局限性，本文提出了一种新的算法，称为改进的局部敏感判别分析（ILSDA）。通过将类内散布矩阵集成到我们的算法中，ILSDA不仅可以保留数据的局部判别邻域结构，而且可以将离群值样本拉到更靠近其类中心的位置，这使其优于原始LSDA和其他状态最先进的算法。在几个公开可用的图像数据集上的大量实验结果表明了我们提出的方法的可行性和有效性。

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  • 来源
    《Multimedia Tools and Applications》 |2015年第1期|85-104|共20页
  • 作者

    Yugen Yi; Baoxue Zhang; Jun Kong; Jianzhong Wang;

  • 作者单位

    College of Computer Science and Information Technology, Northeast Normal University, Changchun, China,School of Mathematics and Statistics, Northeast Normal University, Changchun, China;

    School of Mathematics and Statistics, Northeast Normal University, Changchun, China;

    College of Computer Science and Information Technology, Northeast Normal University, Changchun, China,Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China;

    National Engineering Laboratory for Draggable Gene and Protein Screening, Northeast Normal University, Changchun, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Feature extraction; Outliers; ILSDA; Face image recognition;

    机译：特征提取;离群值;ILSDA;人脸图像识别;

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