一种灵活高效的算法用于正则Fisher判别分析A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis |
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课程网址: | http://videolectures.net/ecmlpkdd09_zhang_fearfda/ |
主讲教师: | Zhihua Zhang |
开课单位: | 浙江大学 |
开课时间: | 2010-09-20 |
课程语种: | 英语 |
中文简介: | Fisher线性判别分析(LDA)及其核扩展(KDA)是一种联合考虑降维和分类的著名方法。尽管广泛应用于实际问题,但围绕其有效实施及其与最小均方误差程序的关系,仍存在未解决的问题。在本文中,我们在正则化估计的框架内解决这些问题。我们的方法可以灵活高效地实现LDA和KDA。我们还发现了正则判别分析和岭回归之间的一般关系。这种关系在传统的基于伪逆的LDA上产生变化,并直接等价于一个普通的最小二乘估计。对一组基准数据集的实验结果证明了我们的方法的有效性。 |
课程简介: | Fisher linear discriminant analysis (LDA) and its kernel extension—kernel discriminant analysis (KDA)—are well known methods that consider dimensionality reduction and classification jointly. While widely deployed in practical problems, there are still unresolved issues surrounding their efficient implementation and their relationship with least mean squared error procedures. In this paper we address these issues within the framework of regularized estimation. Our approach leads to a flexible and efficient implementation of LDA as well as KDA. We also uncover a general relationship between regularized discriminant analysis and ridge regression. This relationship yields variations on conventional LDA based on the pseudoinverse and a direct equivalence to an ordinary least squares estimator. Experimental results on a collection of benchmark data sets demonstrate the effectiveness of our approach. |
关 键 词: | Fisher线性判别分析(LDA); 机器学习; 内核; 正则化 |
课程来源: | 视频讲座网 |
最后编审: | 2020-09-28:heyf |
阅读次数: | 61 |