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Theoretical framework in graph embedding-based discriminant dimensionality reduction
Signal Processing ( IF 4.4 ) Pub Date : 2021-08-14 , DOI: 10.1016/j.sigpro.2021.108289
Guodong Zhao 1 , Zhiyong Zhou 2, 3 , Junming Zhang 4
Affiliation  

Graph embedding-based discriminative dimensionality reduction has remained to be a popular research topic over the past few decades. The weight functions in adjacent graphs are the key to the performance of methods. In practice, the weight functions are always achieved experimentally. Thus far, the selection of effective weight functions has no any theoretical guidance. A theoretical framework considering hypothesis-margin is derived in this study to guide the selection of weight functions, whose truth is verified in a popular algorithm and a more effective supervised discriminant graph embedding-based dimensionality reduction method is introduced. Many experimental results demonstrate the truth of the proposed theoretical framework and the effectiveness of the introduced method. Importantly, the proposed framework can provide theoretical support for the selection of weight functions in the graph embedding-based dimensionality reduction.



中文翻译:

基于图嵌入的判别降维的理论框架

在过去的几十年里,基于图嵌入的判别降维一直是一个热门的研究课题。相邻图中的权重函数是方法性能的关键。在实践中,权重函数总是通过实验实现的。迄今为止,有效权重函数的选择没有任何理论指导。本研究推导出一个考虑假设边界的理论框架来指导权重函数的选择,其真实性在流行的算法中得到验证,并引入了一种更有效的基于监督判别图嵌入的降维方法。许多实验结果证明了所提出的理论框架的真实性和所引入方法的有效性。重要的是,

更新日期:2021-08-21
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