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首页> 外文会议>2011 Eighth International Conference on Computer Graphics, Imaging Visualization >Local Fisher Discriminant Analysis with Maximum Margin Criterion for Image Recognition
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Local Fisher Discriminant Analysis with Maximum Margin Criterion for Image Recognition

机译：具有最大余量准则的局部Fisher判别分析用于图像识别

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摘要

Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in image recognition. Local Fisher Discriminant Analysis (LFDA) is a linear projective map that arises by solving the multimodal problem, which effectively combines the ideas of FDA and LPP. However, since the limited data pairs are employed to determine the discriminative ability, such local discriminative methods usually suffer from the maladjusted learning. To improve the discriminant ability of LFDA, this paper proposed an improved manifold learning method, called local and global marginal discriminant analysis (LGMDA), by incorporating the maximum margin criterion (MMC) for image recognition. As a result, the proposed method tries to find the sub manifold that best discriminates different classes and preserves the intrinsic relations of the local neighborhood in the same class according to prior class information. Experiments on the COIL-20 and YaleB images databases show the effectiveness of the proposed LGMDA.
机译：在不丢失固有信息的情况下降低数据的维数是图像识别中重要的预处理步骤。本地Fisher判别分析（LFDA）是通过解决多峰问题而产生的线性投影图，它有效地结合了FDA和LPP的思想。但是，由于使用有限的数据对来确定判别能力，因此这种局部判别方法通常遭受学习调整不良的困扰。为了提高LFDA的判别能力，本文提出了一种改进的流形学习方法，称为局部和全局边际判别分析（LGMDA），方法是将最大边际判据（MMC）纳入图像识别。结果，所提出的方法试图找到能够最好地区分不同类别并根据先验类别信息保留同一类别中的局部邻域的固有关系的子流形。在COIL-20和YaleB图像数据库上进行的实验表明了所提出的LGMDA的有效性。

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