��������� 2011, 37(9) 216-217,220 DOI:     ISSN: 1000-3428 CN: 31-1289/TP           

����Ŀ¼ | ����Ŀ¼ | ������� | �߼�����                                                            [��ӡ��ҳ]   [�ر�]
Supporting info
[HTML] ����
Email Alert
Article by Zhang, L.
Article by Du, Z. B.
Article by Zhang, D.
Article by Li, Y.
�� ��������ƽ���� ������ ��
(���Ƽ���ѧ���������ѧԺ����� 300222)
ժҪ�� ���䴫���������Դ���������νṹ�����ݼ���Ϊ�ˣ����һ�ֻ���������˹����ӳ��ķ��䴫�������㷨(APPLE)���ڱ�׼���䴫���Ļ�������ǿ����ѧϰ��������ʹ�ò�ؾ���������ݵ�����ƶȣ�����������˹����ӳ������ݼ����н�ά��������ȡ����ͼ�����Ӧ�õ�ʵ����֤����APPLE�ľ���Ч�����ڱ�׼���䴫��������
�ؼ����� ������˹����ӳ��   ���䴫��   Dijkstra�㷨   ��һ������Ϣ  
Affinity Propagation Clustering Based on Laplacian Eigenmaps
ZHANG Liang, DU Zi-ping, ZHANG Jun, LI Yang
(School of Economics and Management, Tianjin University of Science and Technology, Tianjin 300222, China)
Abstract: Affinity propagation is often limited by its inability to cluster datasets with inherent manifold structures. A novel clustering method, namely Affinity Propagation with Laplacian Eigenmaps(APPLE), is proposed to address this problem. It enhances the standard affinity propagation with manifold learning capacity. Geodesic distance is used to compute affinity between data points. Laplacian eigenmaps are applied to reduce the dimensionality and to extract features. Experimental results show APPLE outperforms standard affinity propagation in application of image clustering.
Keywords: Laplacian eigenmaps   Affinity Propagation(AP)   Dijkstra algorithm   Normalized Mutual Information(NMI)  
�ո�����  �޻�����  ����淢������  


���߼��: �� ��(1979��)���У���ʦ����ʿ�����з��򣺻���ѧϰ���˹����ܣ�����ƽ�����ڡ���ʿ����ʿ����ʦ���� ������ ���ʦ����ʿ
ͨѶ����E-mail: zhangliang@tust.edu.cn

[1]Frey B J, Dueck D. Clustering by Passing Messages Between Data Points[EB/OL]. (2007-02-15). http://www.sciecnemag.org.
[2]�� ��, ��ΰ��. �����׺ʹ��ݾ���Ķ�������ʶ�𷽷�[J].���������.2009, 35(14):206-208  ���
[3]Feil B, Abonyi J. Geodesic Distance Based Fuzzy Clustering[J].Advances in Intelligent and Soft Computing.2007, 39:50-59
[4]���ܻ�, ����ά, �� ��, ��. ���ڲ���߾���Ĺ����˹��Laplacian����ӳ��[J].���ѧ��.2009, 20(4):815-824
[5]Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[R]. University of Chicago, Technical Report: TR 2001-01, 2001.
[6]Bousquet O, Chapelle O, Hein M. Measure Based Regulariza- tion[M]. Cambridge, USA: MIT Press, 2004.
[7]Blum A, Chawla S. Learning from Labeled and Unlabeled Data Using Graph Mincuts[C]//Proceedings of the 18th International Conference on Machine Learning. San Francisco, USA: Morgan Kaufmann Publishers, 2001.
1���� ÷;�����.����������˹����ӳ��ķ��������[J]. ���������, 2009,35(16): 178-179


Copyright by ���������