کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6938041 | 1449921 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Incremental generalized multiple maximum scatter difference with applications to feature extraction
ترجمه فارسی عنوان
حداکثر اختلاف پراکنده چندگانه افزایشی با برنامه های کاربردی برای استخراج ویژگی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we propose a new algorithm to implement the generalized multiple maximum scatter difference (GMMSD). Due to enhanced features of this algorithm over the original GMMSD, we named it GMMSD+. By employing a different projection from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix, GMMSD+ can divide the centroid vector of each class into two components: intrinsic common component (ICC) and discriminant difference component (DCC), and then automatically discards ICC which contains little discriminative information, while keeping DCC which contains the true discriminative power. Next, we introduce a practical implementation of GMMSD+, which can accurately and efficiently update the discriminant vectors with new training samples incrementally, eliminating the complete re-computation of the training process. Our experiments demonstrate that incremental version of GMMSD+(IGMMSD+) eliminates the complete re-computation of the training process when new training samples are presented, leading to significantly reduced computational cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 55, August 2018, Pages 67-79
Journal: Journal of Visual Communication and Image Representation - Volume 55, August 2018, Pages 67-79
نویسندگان
Ning Zheng, Xin Guo, Yun Tie, Nan Dong, Lin Qi, Ling Guan,