Fuzzy Uncorrelated Discriminant Transformation and Its Application
模糊非相关判别转换及其应用

Linear discriminant analysis is a way of feature extraction and dimension reduction. It is widely applied in face recognition, speech recognition, and handwriting recognition etc. However, many linear discriminant analyses are "hard" ones and every data point belongs to one class or another class strictly. In this paper, a fuzzy uncorrelated discriminant transformation (FUDT) is proposed based on uncorrelated discriminant transformation (UDT). FUDT is a supervised learning method with fuzzy set and its discriminant vectors satisfy the equation of generalized rayleigh quotient. Furthermore, the projection of samples to fuzzy uncorrelated optimal discriminant vectors is uncorrelated by FUDT. The experimental results show that FUDT is better than UDT in extracting the feature of SAR images which come from MSTAR.