کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942050 | 1436982 | 2017 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Low-rank decomposition meets kernel learning: A generalized Nyström method
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Low-rank matrix decomposition and kernel learning are two useful techniques in building advanced learning systems. Low-rank decomposition can greatly reduce the computational cost of manipulating large kernel matrices. However, existing approaches are mostly unsupervised and do not incorporate side information such as class labels, making the decomposition less effective for a specific learning task. On the other hand, kernel learning techniques aim at constructing kernel matrices whose structure is well aligned with the learning target, which improves the generalization performance of kernel methods. However, most kernel learning approaches are computationally very expensive. To obtain the advantages of both techniques and address their limitations, in this paper we propose a novel kernel low-rank decomposition formulation called the generalized Nyström method. Our approach inherits the linear time and space complexity via matrix decomposition, while at the same time fully exploits (partial) label information in computing task-dependent decomposition. In addition, the resultant low-rank factors can generalize to arbitrary new samples, rendering great flexibility in inductive learning scenarios. We further extend the algorithm to a multiple kernel learning setup. The experimental results on semi-supervised classification demonstrate the usefulness of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence - Volume 250, September 2017, Pages 1-15
Journal: Artificial Intelligence - Volume 250, September 2017, Pages 1-15
نویسندگان
Liang Lan, Kai Zhang, Hancheng Ge, Wei Cheng, Jun Liu, Andreas Rauber, Xiao-Li Li, Jun Wang, Hongyuan Zha,