کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532396 869947 2012 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel supervised dimensionality reduction algorithm: Graph-based Fisher analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A novel supervised dimensionality reduction algorithm: Graph-based Fisher analysis
چکیده انگلیسی

In this paper, a novel supervised dimensionality reduction (DR) algorithm called graph- based Fisher analysis (GbFA) is proposed. More specifically, we redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the not-same-class points to the penalty graph by a strictly monotone decreasing function; then the novel feature extraction criterion based on the intrinsic and penalty graph is applied. For the non-linearly separable problems, we study the kernel extensions of GbFA with respect to positive definite kernels and indefinite kernels, respectively. In addition, experiments are provided for analyzing and illustrating our results.


► A novel feature extraction criterion based on the Spectral Graph Theory is proposed.
► GbFA algorithm derivation for the small sample size cases is specified.
► We extended the kernel GbFA model for the linear non-separated problem.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 45, Issue 4, April 2012, Pages 1471–1481
نویسندگان
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