计算机技术与发展Issue(1)：73-76,4.DOI:10.3969/j.issn.1673-629X.2014.01.019
张量线性判别分析算法研究
Research of Tensor Linear Discriminant Analysis Algorithm
摘要
Abstract
Aiming at problems of small sample existed in the traditional linear discriminant analysis and two projection matrixes of Ten-sorLDA algorithms cannot calculate,low-dimensional feature extraction is not sufficient,study and implement TensorLDA based on ten-sor subspace. And the It-TensorLDA algorithm is presented,which first initializes with unit matrix,then uses the optimized criterion to get another projection matrix,carrying on many times iteration. Apply ORL human dataset to test the performance of algorithm. The ex-periments show that in ORL dataset It-TensorLDA is 1. 88% higher than TensorLDA and 3. 03% compared with Fisherfaces. So,the al-gorithm avoids the small sample problem,enhances the efficiency of face recognition.关键词
线性判别分析/张量/子空间/张量线性判别分析/特征提取Key words
linear discriminant analysis/tensor/subspace/tensor linear discriminant analysis/feature extraction分类
信息技术与安全科学引用本文复制引用
赵越,徐鑫,乔利强..张量线性判别分析算法研究[J].计算机技术与发展,2014,(1):73-76,4.基金项目
国家自然科学基金资助项目(60970157) （60970157）
