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Title: Adaptive classification algorithm based on maximum scatter difference discriminant criterion
Authors: Song, FX
Zhang, D 
Yang, JY
Gao, XM
Keywords: Adaptive algorithm
Face recognition
Fisher discriminant criterion
Large margin linear projection
Machine learning
Maximum scatter difference
Issue Date: 2006
Publisher: 科學出版社
Source: 自动化学报 (Acta automatica sinica), 2006, v. 32, no. 4, p. 541-549 How to cite?
Journal: 自动化学报 (Acta automatica sinica) 
Abstract: In this paper we first prove that the optimal discriminant direction of Maximum scatter difference (MSD) discriminant criterion with a certain value c0 is equivalent to the optimal Fisher discriminant direction. Second, sample recognition rate curves of MSD are illustrated. The recognition rate curve is usually a pulse curve when the within-class scatter matrix is nonsingular. With the increase of parameter C, the recognition rate of MSD also increases. The recognition rate of MSD achieves its maximum when C is equal to c0. In addition, former study showed that, when the within-class scatter matrix is singular, MSD criterion is approaching the large margin linear projection criterion as parameter C increases. Moreover, the recognition rate curve of MSD is non-decreasing. Thus, an adaptive classification algorithm based on maximum scatter difference discriminant criterion is proposed based on these facts. The new algorithm can tune parameter C automatically according to the characteristics of training samples. Experiment conducted on 6 datasets from UCI Machine Learning Repository and AR face database demonstrates that the adaptive classification algorithm for maximum scatter difference has good classification property.
ISSN: 0254-4156
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