کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6958915 | 1451947 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multiple graph unsupervised feature selection
ترجمه فارسی عنوان
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Feature selection improves the quality of the model by filtering out the noisy or redundant part. In the unsupervised scenarios, the selection is challenging due to the unavailability of the labels. To overcome that, the graphs which can unfold the geometry structure on the manifold are usually used to regularize the selection process. These graphs can be constructed either in the local view or the global view. As the local graph is more discriminative, previous methods tended to use the local graph rather than the global graph. But the global graph also has useful information. In light of this, in this paper, we propose a multiple graph unsupervised feature selection method to leverage the information from both local and global graphs. Besides that, we enforce the l2,p norm to achieve more flexible sparse learning. The experiments which inspect the effects of multiple graph and l2,p norm are conducted respectively on various datasets, and the comparisons to other mainstream methods are also presented in this paper. The results support that the multiple graph could be better than the single graph in the unsupervised feature selection, and the overall performance of the proposed method is higher than the other comparisons.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 120, March 2016, Pages 754-760
Journal: Signal Processing - Volume 120, March 2016, Pages 754-760
نویسندگان
Xingzhong Du, Yan Yan, Pingbo Pan, Guodong Long, Lei Zhao,