Kernel-driven similarity learning

被引:107
作者
Kang, Zhao [1 ,2 ]
Peng, Chong [2 ]
Cheng, Qiang [2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Southern Illinois Univ, Dept Comp Sci, Carbondale, IL 62901 USA
基金
美国国家科学基金会;
关键词
Similarity measure; Nonlinear relation; Sparse representation; Kernel method; Multiple kernel learning; Clustering; Recommender systems; SPARSE REPRESENTATION; SIGNAL RECOVERY;
D O I
10.1016/j.neucom.2017.06.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Similarity measure is fundamental to many machine learning and data mining algorithms. Predefined similarity metrics are often data-dependent and sensitive to noise. Recently, data-driven approach which learns similarity information from data has drawn significant attention. The idea is to represent a data point by a linear combination of all (other) data points. However, it is often the case that more complex relationships beyond linear dependencies exist in the data. Based on the well known fact that kernel trick can capture the nonlinear structure information, we extend this idea to kernel spaces. Nevertheless, such an extension brings up another issue: its algorithm performance is largely determined by the choice of kernel, which is often unknown in advance. Therefore, we further propose a multiple kernel-based learning method. By doing so, our model can learn both linear and nonlinear similarity information, and automatically choose the most suitable kernel. As a result, our model is capable of learning complete similarity information hidden in data set. Comprehensive experimental evaluations of our algorithms on clustering and recommender systems demonstrate its superior performance compared to other state-ofthe-art methods. This performance also shows the great potential of our proposed algorithm for other possible applications. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:210 / 219
页数:10
相关论文
共 55 条
[1]   A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem [J].
Ahn, Hyung Jun .
INFORMATION SCIENCES, 2008, 178 (01) :37-51
[2]  
[Anonymous], 2011, INTRO RECOMMENDER SY
[3]  
[Anonymous], COMPUTATIONAL INTELL
[4]   THEORY OF REPRODUCING KERNELS [J].
ARONSZAJN, N .
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY, 1950, 68 (MAY) :337-404
[5]   A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [J].
Beck, Amir ;
Teboulle, Marc .
SIAM JOURNAL ON IMAGING SCIENCES, 2009, 2 (01) :183-202
[6]  
Bellogin Alejandro, 2011, Proceedings of the fifth ACM conference on Recommender systems, P333
[7]   Distributed optimization and statistical learning via the alternating direction method of multipliers [J].
Boyd S. ;
Parikh N. ;
Chu E. ;
Peleato B. ;
Eckstein J. .
Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122
[8]  
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
[9]  
Cai D, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1010
[10]   Heterogeneous Image Features Integration via Multi-Modal Semi-Supervised Learning Model [J].
Cai, Xiao ;
Nie, Feiping ;
Cai, Weidong ;
Huang, Heng .
2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2013, :1737-1744