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首页> 外文会议>International Workshop on Intelligent Computing in Pattern Analysis/Synthesis(IWICPAS 2006); 20060826-27; Xi'an(CN) >Discriminant Transform Based on Scatter Difference Criterion in Hidden Space
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Discriminant Transform Based on Scatter Difference Criterion in Hidden Space

机译：隐藏空间中基于散射差异准则的判别变换

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摘要

In this paper, a novel feature extraction method based on scatter difference criterion in hidden space is developed. Its main idea is that the original input space is first mapped into a hidden space through a kernel function, where the feature extraction is conducted using the difference of between-class scatter and within-class scatter as the discriminant criterion. Different from the existing kernel-based feature extraction methods, the kernel function used in the proposed one is not required to satisfy Mercer's theorem so that they can be chosen from a wide range. It is more important that due to adoption of the scatter difference as the discriminant criterion for feature extraction, the proposed method essentially avoids the small size samples problem usually occurred in the kernel Fisher discriminant analysis. Finally, extensive experiments are performed on a subset of FERET face database. The experimental results indicate that the proposed method outperforms the traditional scatter difference discriminant analysis in recognition performance.
机译：本文提出了一种基于散度差准则的隐藏空间特征提取方法。其主要思想是，首先通过核函数将原始输入空间映射到隐藏空间，然后使用类间散布和类内散布之差作为判别准则进行特征提取。与现有的基于核的特征提取方法不同，在拟议的方法中使用的核函数不需要满足Mercer定理，因此可以从广泛的范围中进行选择。更重要的是，由于采用了散射差异作为特征提取的判别标准，因此该方法从根本上避免了核Fisher判别分析中通常会出现的小样本问题。最后，在FERET人脸数据库的子集上进行了广泛的实验。实验结果表明，该方法在识别性能上优于传统的散度差判别分析。

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