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.

Packet : 814045772dlda_pk_lda_for_feature_extraction.zip filelist
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

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