基于边界检测的多流形学习算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201611009

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Abstract In manifold learning algorithms, the data are assumed to be aligned on a single manifold. The application of algorithms is limited due to the general distribution of practical datasets on multiple manifolds. In this paper, multi-manifold learning based on boundary detection(MBD) is proposed. By the proposed method, data of distribution on several manifolds are efficiently learned through boundary detection and intra and inter manifolds geodesic distances can be kept faithfully. Firstly the boundary of data manifolds is detected and then the dimensionality of the manifolds is reduced separately. Finally, low dimensional coordinates are relocated into a global coordinate system. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated through experiments on both synthetic and real datasets.

Key words： Multi-manifold Learning Boundary Detection Geodesic Distance

Received: 15 May 2016

Fund:Supported by National Natural Science Foundation of China (No.61033013,60775045), Soochow Scholar Program of Soochow University (No.14317360)

About author:: ZOU PengCorresponding author, born in 1993, master student. His research interests include Lie group machine lear-ning and manifold learning.
LI Fanzhang, born in 1964, Ph.D., professor. His research interests include Lie group machine learning and dynamic fuzzy logic.
YIN Hongwei, born in 1990. Ph.D. candidate. His research interests include spectral machine learning.
ZHANG Li, born in 1975, Ph.D., professor. Her research interests include pattern recognition, machine learning and data mining.
ZHANG Zhao, born in 1984, Ph.D., associate professor. His research interests include pattern recognition, machine learning, data mining and computer vision.

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	ZOU Peng
	LI Fanzhang
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	ZHANG Li
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Cite this article:

ZOU Peng,LI Fanzhang,YIN Hongwei等. Multi-manifold Learning Based on Boundary Detection[J]. , 2016, 29(11): 1037-1047.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201611009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I11/1037

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