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- W36951687 abstract "Kernel Fisher discriminant (KFD) is a state-of-the-art nonlinear machine learning method, and it has great potential to outperform linear Fisher discriminant. In this paper, a nonlinear discriminative texture feature extraction method based on KFD is proposed for texture classication. We mathematically show that nding the optimal discriminative texture features is equivalent to nding the optimal discriminative projection directions of the input data by KFD. Our proposed KFD-based method integrates texture feature extraction, nonlinear dimensionality reduction, and discrimination in a unied framework. Optimized and closed-form solutions are derived for both two-class and multi-class texture classication problems, individually. Extensive experimental results clearly show that the proposed method yields excellent performance in texture classication and outperforms other texture recognition methods based on discriminative classiers." @default.
- W36951687 created "2016-06-24" @default.
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- W36951687 date "2004-01-01" @default.
- W36951687 modified "2023-09-24" @default.
- W36951687 title "Kernel Fisher Discriminant for Texture Classication" @default.
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