کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1147498 | 957766 | 2012 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
On the regularized Laplacian eigenmaps
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
To find an appropriate low-dimensional representation for complex data is one of the central problems in machine learning and data analysis. In this paper, a nonlinear dimensionality reduction algorithm called regularized Laplacian eigenmaps (RLEM) is proposed, motivated by the method for regularized spectral clustering. This algorithm provides a natural out-of-sample extension for dealing with points not in the original data set. The consistency of the RLEM algorithm is investigated. Moreover, a convergence rate is established depending on the approximation property and the capacity of the reproducing kernel Hilbert space measured by covering numbers. Experiments are given to illustrate our algorithm.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 7, July 2012, Pages 1627–1643
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 7, July 2012, Pages 1627–1643
نویسندگان
Ying Cao, Di-Rong Chen,