一种自适应加权的双向二维线性鉴别分析算法

二维线性鉴别分析是一种直接基于矩阵的特征提取方法,有效地提高了特征提取速度且避免了小样本问题,但是提取的特征向量维数高,不利于后期分类,而且获得的最佳投影矩阵只是来自于图像的列信息.另外,不同的样本在求取最佳投影矩阵时,所起的作用是不一样的,因此提出了一种自适应加权的双向二维线性鉴别分析算法,即是对图像矩阵顺序地进行水平和垂直2个方向的二维线性鉴别分析,自适应加权处理则是使不同的样本带有不同的权值,以提高样本在低维线性空间中的可分性.在ORL和Yale人脸库上的实验结果表明,改进的算法在降低了原算法提取的特征向量维数的同时,较原二维线性鉴别分析的识别性能有了较明显的改善.

A bidirectional 2-D linear discriminant analysis algorithm based on an adaptively weighted function

Two-dimensional linear discriminant analysis extracts feature vectors directly from an image matrix.This improves the speed of feature vector extraction and also eliminates problems posed by small samples.However,the quantity of feature vector data obtained using this method quickly become so large as to make the classification task difficult,and the optimal projective matrix can only be derived from the column direction of the image.Moreover,different samples have different effects on the optimal projective matrix.To solve these weaknesses,we propose a two-dimensional linear discriminant algorithm based on an adaptively weighted function,whereby the image matrix is analyzed bidirectionally.Two-dimensional linear discriminant analysis is done in the horizontal and vertical directions sequentially;the different samples are given different weights so as to improve the performance of classification in the low-dimensional linear space.Numerical experiments on the ORL and Yale facial databases showed that the proposed method outperforms the original two-dimensional linear discriminant analysis algorithm while reducing the feature vector's dimensions.