کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6938444 | 869578 | 2016 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Kernel dictionary learning based discriminant analysis
ترجمه فارسی عنوان
تجزیه و تحلیل مبتنی بر یادگیری مبتنی بر فرهنگ هسته ای
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کلمات کلیدی
روش کرنل، نمایندگی انحصاری، یادگیری فرهنگ لغت یادگیری ویژگی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Sparse representation based classification (SRC) has been successfully applied in many applications. But how to determine appropriate features that can best work with SRC remains an open question. Dictionary learning (DL) has played an import role in the success of sparse representation, while SRC treats the entire training set as a structured dictionary. In addition, as a linear algorithm, SRC cannot handle the data with highly nonlinear distribution. Motivated by these concerns, in this paper, we propose a novel feature learning method (termed kernel dictionary learning based discriminant analysis, KDL-DA). The proposed algorithm aims at learning a projection matrix and a kernel dictionary simultaneously such that in the reduced space the sparse representation of the data can be easily obtained, and the reconstruction residual can be further reduced. Thus, KDL-DA can achieve better performances in the projected space. Extensive experimental results show that our method outperforms many state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 40, Part B, October 2016, Pages 470-484
Journal: Journal of Visual Communication and Image Representation - Volume 40, Part B, October 2016, Pages 470-484
نویسندگان
Guoqing Zhang, Huaijiang Sun, Zexuan Ji, Guiyu Xia, Lei Feng, Quansen Sun,