基于概率类和不相关判别的半监督局部Fisher 方法

Fisher 判别分析是统计模式识别中经典的有监督维数约简方法, 可以在最大化类间散度的同时最小化类内散度, 但存在分析过程中仅使用有标记数据而忽略无标记数据的问题. 鉴于此, 提出基于概率类和不相关判别的半监督局部Fisher (SLFisher) 方法, 以实现半监督学习的高维映射到低维的类间数据对尽可能地分离, 且类内邻近数据尽可能地紧凑. 采用2 组标准数据集进行实验, 结果表明了SLFisher 方法能够有效提高识别率.

Semi-supervised local Fisher method based on probability class and uncorrelated discriminant

The Fisher discriminant analysis(FDA) is a classical supervised dimensionality reduction method in statistical pattern recognition. The FDA can maximize the scatter between different classes, while minimizing the scatter within each class, but the analysis process of the FDA only utilizes the labeled data and ignores the unlabeled data. Therefore, a semi-supervised local fisher method based on probability class and uncorrelated discriminant(SLFisher), which enables the data pairs in different classes to be separated from each other and the nearby data pairs in the same class to be closed after dimensionality reduction. Two benchmark datasets are applied in the experiment, and the results show that the SLFisher can greatly improve recognition rate.