✦ LIBER ✦

A reformative kernel Fisher discriminant analysis

✍ Scribed by Yong Xu; Jing-yu Yang; Jian Yang

Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 133 KB
Volume: 37
Category: Article
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2003.10.006

No coin nor oath required. For personal study only.

✦ Synopsis

A reformative kernel Fisher discriminant method is proposed, which is directly derived from the naive kernel Fisher discriminant analysis with superiority in classiÿcation e ciency. In the novel method only a part of training patterns, called "signiÿcant nodes", are necessary to be adopted in classifying one test pattern. A recursive algorithm for selecting "signiÿcant nodes", which is the key of the novel method, is presented in detail. The experiment on benchmarks shows that the novel method is e ective and much e cient in classifying.

📜 SIMILAR VOLUMES

Optimally regularised kernel Fisher disc

Optimally regularised kernel Fisher discriminant classification

✍ Kamel Saadi; Nicola L.C. Talbot; Gavin C. Cawley 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 694 KB

Essence of kernel Fisher discriminant: K

Essence of kernel Fisher discriminant: KPCA plus LDA

✍ Jian Yang; Zhong Jin; Jing-yu Yang; David Zhang; Alejandro F Frangi 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 143 KB

A novel method for Fisher discriminant a

A novel method for Fisher discriminant analysis

✍ Yong Xu; Jing-Yu Yang; Zhong Jin 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 127 KB

A note on kernel uncorrelated discrimina

A note on kernel uncorrelated discriminant analysis

✍ Wenming Zheng 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 166 KB

Subspace Semi-supervised Fisher Discrimi

Subspace Semi-supervised Fisher Discriminant Analysis

✍ Wu-Yi YANG; Wei LIANG; Le XIN; Shu-Wu ZHANG 📂 Article 📅 2009 🏛 Elsevier ⚖ 333 KB

Fisher discriminant analysis (FDA) is a popular method for supervised dimensionality reduction. FDA seeks for an embedding transformation such that the ratio of the between-class scatter to the within-class scatter is maximized. Labeled data, however, often consume much time and are expensive to obt

An efficient kernel discriminant analysi

An efficient kernel discriminant analysis method

✍ Juwei Lu; K.N. Plataniotis; A.N. Venetsanopoulos; Jie Wang 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 167 KB

Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, w