Parameterized Discriminant Analysis Methods

Parameterized Discriminant Analysis Methods

David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
DOI: 10.4018/978-1-60566-200-8.ch005
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In this chapter, we mainly present three kinds of weighted LDA methods. In Sections 5.1, 5.2 and 5.3, we respectively present parameterized direct linear discriminant analysis, weighted nullspace linear discriminant analysis and weighted LDA in the range of within-class scatter matrix. We offer a brief summery of the chapter in Section 5.4.
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Parameterized Direct Linear Discriminant Analysis


Direct LDA (D-LDA) (Yu & Yang, 2001) is an important feature extraction method for SSS problems. It first maps samples into the range of the between-class scatter matrix, and then transforms these projections using a series of regulating matrices. D-LDA can efficiently extract features directly from a high-dimensional input space without the need to first apply other dimensionality reduction techniques such as PCA transformations in Fisherfaces (Belhumeur, Hespanha, & Kriengman, 1997) or pixel grouping in nullspace LDA (N-LDA) (Chen, Liao, Ko, Lin, & Yu, 2000), and as a result has aroused the interest of many researchers in the field of pattern recognition and computer vision. Indeed, there are now many extensions of D-LDA, such as fractional D-LDA (Lu, Plataniotis, & Venetsanopoulos, 2003a), regularized D-LDA (Lu, Plataniotis, &Venetsanopoulos, 2003b; Lu, Plataniotis, & Venetsano-poulos, 2005), kernel D-LDA (Lu, Plataniotis, & Venetsanopoulos, 2003c), and boosting D-LDA (Lu, Plataniotis, Venetsanopoulos, & Li, 2006).

But there nonetheless remain some questions as to its usefulness as a facial feature extraction method. First, as been pointed out in Lu, Plataniotis and Venetsanopoulos (2003b; Lu, Plataniotis, & Venetsanopoulos, 2005), D-LDA performs badly when only two or three samples per individual are used. Second, regulating matrices in D-LDA are either redundant or probably harmful. The second drawback of D-LDA has not been seriously addressed in previous studies.

In this section, we present a new feature extraction method—parameterized direct linear discriminant analysis (PD-LDA) for SSS problems (Song, Zhang, Wang, Liu, & Tao, 2007). As an improvement of D-LDA, PD-LDA inherits advantages of D-LDA such as “direct” and “efficient”. Meanwhile, it greatly enhances the accuracy and robustness of D-LDA.


Direct Linear Discriminant Analysis

The Algorithm of D-LDA

Let 978-1-60566-200-8.ch005.m01 and 978-1-60566-200-8.ch005.m02 denote the between- and the within-class scatter matrices respectively. The calculation procedure of D-LDA is as follows:

Step 1. Perform eigenvalue decomposition on the between-class scatter matrix 978-1-60566-200-8.ch005.m03

Let 978-1-60566-200-8.ch005.m04 be the eigenvalue matrix of 978-1-60566-200-8.ch005.m05 in decreasing order and 978-1-60566-200-8.ch005.m06978-1-60566-200-8.ch005.m07 be the corresponding eigenvector matrix. It follows that

978-1-60566-200-8.ch005.m08. (1)

Let 978-1-60566-200-8.ch005.m09 be the rank of the matrix 978-1-60566-200-8.ch005.m10. Let 978-1-60566-200-8.ch005.m11 and 978-1-60566-200-8.ch005.m12978-1-60566-200-8.ch005.m13, and we have

978-1-60566-200-8.ch005.m14. (2)

Step 2. Map each sample vector 978-1-60566-200-8.ch005.m15 to get its intermediate representation 978-1-60566-200-8.ch005.m16 using the projection matrix 978-1-60566-200-8.ch005.m17

Step 3. Perform eigenvalue decomposition on the within-class scatter matrix of the projected samples, 978-1-60566-200-8.ch005.m18 which is given by

978-1-60566-200-8.ch005.m19. (3)

Let 978-1-60566-200-8.ch005.m20 be the eigenvalue matrix of 978-1-60566-200-8.ch005.m21 in ascending order and 978-1-60566-200-8.ch005.m22978-1-60566-200-8.ch005.m23 be the corresponding eigenvector matrix. It follows that

978-1-60566-200-8.ch005.m24. (4)

Step 4. Calculate the discriminant matrix 978-1-60566-200-8.ch005.m25 and map each sample 978-1-60566-200-8.ch005.m26 to 978-1-60566-200-8.ch005.m27The discriminant matrix of D-LDA is given by

978-1-60566-200-8.ch005.m28. (5)

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