Optimal fisher discriminant analysis using the rank decomposition

Cheng, Yujie; Zhuang, Yong-Ming; Yang, Jingyu

doi:10.1016/0031-3203(92)90010-g

Cited by 61 publications

(43 citation statements)

References 5 publications

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“…The detailed deductions can be seen from Appendix A and the results are given by the following formulations: (17) (18) (19) where is an by 1 column vector (all terms equal to 1).…”

Section: Direct Kernel Biased Discriminant Analysis (Dkbda) and mentioning

confidence: 99%

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Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

Tao

Tang

et al. 2006

IEEE Trans. Multimedia

165

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Abstract-In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms.

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“…The detailed deductions can be seen from Appendix A and the results are given by the following formulations: (17) (18) (19) where is an by 1 column vector (all terms equal to 1).…”

Section: Direct Kernel Biased Discriminant Analysis (Dkbda) and mentioning

confidence: 99%

“…In the last 20 years, Fisher linear discriminant analysis (LDA) has been successfully used in face recognition [19]- [23], [26]. LDA was first used in CBIR for feature selection and extracts the most discriminant subset feature for image retrieval.…”

mentioning

confidence: 99%

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

Tao

Tang

et al. 2006

IEEE Trans. Multimedia

165

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“…Horn and Schunck [66] developed an iterative method for computing optical flow field using the regularization approach. Subsequently Anandan [11] has presented hierarchical approaches to the computation of the optical flow field.…”

Section: Discussionmentioning

confidence: 99%

Face recognition technology for law enforcement applications

Wilson¹,

Barnes²,

Chellappa³

et al. 1994

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“…Although eigenfaces and singular value vector have good properties for representing images, they are not quite suitable for recognizing images. Based on this viewpoint, Cheng presented an efficient approach to human face recognition based on projective images [6]. Also Foley-Sammon transform (FST) has been considered as an excellent method of dimensionality reduction in terms of discriminant information content [7]- [13].…”

Section: Introductionmentioning

confidence: 99%

A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition

Wu¹,

Kittler²,

Yang³

et al. 2004

Procedings of the British Machine Vision Conference 2004

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We propose a new kernel direct discriminant analysis (KDDA) algorithm in this paper. First, a recently advocated direct linear discriminant analysis (DLDA) algorithm is overviewed. Then the new KDDA algorithm is developed which can be considered as a kernel version of the DLDA algorithm. The design of the minimum distance classifier in the new kernel subspace is then discussed. The results of experiments on two well-known facial databases show the effectiveness of the proposed method in face recognition. The results of experiments also confirm that DLDA can be viewed as a special case of the proposed KDDA algorithm.

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Optimal fisher discriminant analysis using the rank decomposition

Cited by 61 publications

References 5 publications

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

Face recognition technology for law enforcement applications

A New Kernel Direct Discriminant Analysis (KDDA) Algorithm for Face Recognition

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