Non-Iterative Two-Dimensional Linear Discriminant Analysis
Non-Iterative Two-Dimensional Linear Discriminant Analysis
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DOI：
10.1109/icpr.2006.860
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发表时间：
2006-08
期刊：
影响因子：
--
通讯作者：
K. Inoue;K. Urahama
中科院分区：
文献类型：
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作者：
K. Inoue;K. Urahama
线性判别分析（LDA）是一种众所周知的向量空间中标记数据的特征提取和降维方案。 LDA 已扩展为二维 LDA (2DLDA)，它是一种矩阵表示数据的迭代算法。在本文中，我们提出了 2DLDA 的非迭代算法。实验结果表明，非迭代算法的识别率与迭代 2DLDA 相当，并且计算效率比迭代 2DLDA 更高。
Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. LDA has been extended to two-dimensional LDA (2DLDA), which is an iterative algorithm for data in matrix representation. In this paper, we propose non-iterative algorithms for 2DLDA. Experimental results show that the non-iterative algorithms achieve competitive recognition rates with the iterative 2DLDA, while they are computationally more efficient than the iterative 2DLDA