基于模糊集理论的二维线性鉴别分析新方法

二维线性鉴别分析（2DLDA）是一种直接基于矩阵的特征提取方法，跳过传统的基于Fisher鉴别准则的线性鉴别分析方法中必须先将二维矩阵转化成一维矢量的过程，有效地提高了特征提取速度且避免了小样本问题，其识别率优于传统的Fisherface方法。结合模糊集理论，提出了一种新的2DLDA算法——模糊2DLDA（FIDLDA）算法。首先采用FKNN算法得到相应的样本分布信息，并按其对最后得到的特征向量所作的贡献融入到特征抽取过程中，得到有效的样本特征向量集。实验表明，F2DLDA算法的性能优于传统的2DLDA算法和Fisherface方法。

2DLDA algorithm is based on 2D matrices and overleaps the step of transforming the matrices into the corresponding vectors,which is done on conventional LDA algorithm.However,performance of recognition rate may always be degraded by the overlapping(outlier) samples et al in the field of pattern recognition.How to avoid these shortcomings and extract optimal features to improve the performance of recognition is a key step.In this paper,a new 2DLDA algorithm, named fuzzy 2DLDA,is proposed.Fuzzy k-nearest neig...

国家自然科学基金资助项目(60472060)