کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4977374 1451925 2018 6 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
Semi-supervised graph-based retargeted least squares regression
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Semi-supervised graph-based retargeted least squares regression
چکیده انگلیسی


- A semi-supervised graph-based retargeted least squares regression model is proposed for multicategory classification.
- Our aim is to utilize a graph regularization to restrict the regression labels of ReLSR, such that similar samples should have similar regression labels.
- Linear squares regression and graph construction are unified into a same framework to achieve an overall optimum.

In this paper, we propose a semi-supervised graph-based retargeted least squares regression model (SSGReLSR) for multicategory classification. The main motivation behind SSGReLSR is to utilize a graph regularization to restrict the regression labels of ReLSR, such that similar samples should have similar regression labels. However, in SSGReLSR, constructing the graph structure and learning the regression matrix are two independent processes, which can't guarantee an overall optimum. To overcome this shortage of SSGReLSR, we also propose a semi-supervised graph learning retargeted least squares regression model (SSGLReLSR), where linear squares regression and graph construction are unified into a same framework to achieve an overall optimum. To optimize our proposed SSGLReLSR, an efficient iteration algorithm is proposed. Extensive experiments results confirm the effectiveness of our proposed methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 142, January 2018, Pages 188-193
نویسندگان
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