文件名称:2DLDA_PK_LDA_for_feature_extraction
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2008-10-13
- 文件大小：
- 12.31kb
- 下载次数：
- 0次
- 提 供 者：
- r***
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
These are the codes in \"A note on two-dimensional linear discrimant analysis\", Pattern Recognition Letter
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem on the other hand, we compare several distance measures when the feature matrices and feature vectors are adopted.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
压缩包 : 814045772dlda_pk_lda_for_feature_extraction.zip 列表 2DLDA PK LDA for feature extraction/ADM1.m 2DLDA PK LDA for feature extraction/ADM2.m 2DLDA PK LDA for feature extraction/Fnorm.m 2DLDA PK LDA for feature extraction/Fnorm2.m 2DLDA PK LDA for feature extraction/GetFullRankMatrix.m 2DLDA PK LDA for feature extraction/iterative2DLDA.asv 2DLDA PK LDA for feature extraction/iterative2DLDA.m 2DLDA PK LDA for feature extraction/LDA2D.m 2DLDA PK LDA for feature extraction/MiniClassfiers.m 2DLDA PK LDA for feature extraction/OLDA.m 2DLDA PK LDA for feature extraction/randdatagenerate.m 2DLDA PK LDA for feature extraction/ULDA.m 2DLDA PK LDA for feature extraction/VM1.m 2DLDA PK LDA for feature extraction/VM2.m