基于zp范数最大化的线性判别分析

Proceedings of 2nd International Conference on Information Technology and Electronic Commerce Pub Date : 2014-12-01 DOI:10.1109/ICITEC.2014.7105578

Lei-Lei An, Hong-Jie Xing

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引用次数: 4

摘要

本文提出了基于lp -范数(LDA- lp)优化方法的线性判别分析(LDA)。研究了利用任意p值的lp -范数的目标函数。通过最大化基于lp范数的类间散射与类内散射的比值，LDA-Lp可以构造一组局部最优投影向量。利用梯度上升法得到最优投影向量。在两个合成数据集和14个基准数据集上的实验结果表明，通过选择p的最优值可以获得更好的LDA-Lp性能。

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Linear discriminant analysis based on Zp-norm maximization

In this paper, linear discriminant analysis (LDA) based on Lp-norm (LDA-Lp) optimization method is proposed. The objective function utilizing the Lp-norm with arbitrary p value is studied. By maximizing the Lp-norm-based ratio between the between-class scatter and the within-class scatter, LDA-Lp can construct a set of local optimal projection vectors. Moreover, the optimal projection vectors can be obtained by the gradient ascent method. Experimental results on the two synthetic and fourteen benchmark datasets demonstrate that the better performance of LDA-Lp can be achieved by choosing the optimal value of p.

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Proceedings of 2nd International Conference on Information Technology and Electronic Commerce

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