基于秩空间差异的多核组合方法

doi:10.6040/j.issn.1672-3961.0.2020.248

摘要/Abstract

摘要： 在求解秩空间差异性的基础上,提出一种基于秩空间差异性的多核组合方法。将样本按照特征进行分组,使用不同的核函数对已完成分组的数据进行训练,并应用网格搜索法对核函数的参数进行寻优。在备选核函数中选取两个核函数,将分为两组的数据分别放入对应的核函数中进行映射,通过判断数据经过核函数映射后的秩空间差异性为基础核函数的选择提供参考。选用白酒数据集、乳腺癌数据集以及葡萄酒品质数据集进行试验,验证了当数据经过已选的基础核函数映射后,秩空间差异越大,分类的准确率越高。试验结果表明应用该方法进行基础核函数的选择以及组合的可行性。

关键词: 多核学习, 秩空间差异性, 多核组合, 网格搜索

Abstract: A multi-kernel combination method based on rank spatial difference was proposed in this paper. samples were grouped according to characteristics, different kernel functions are used to train the grouped data, and the parameters of the kernel function are optimized by grid search method. Two kernel functions were selected from the alternative kernel functions, and the data divided into two groups were respectively put into the corresponding kernel function for mapping. Then the rank spatial difference of the data after the kernel function mapping was judged to provide reference for the selection of the basic kernel function. The wine data set, the breast cancer data set and the wine quality data set were selected for the experiment to verify that when the data were mapped by the selected basic kernel function, the greater the rank space difference was, the higher the classification accuracy was. The experimental results showed that the method was feasible for the selection and combination of basic kernel functions.

Key words: multi-kernel learning, rank spatial difference, multi-kernel combination, grid search

中图分类号:

TP391

王梅,薛成龙,张强. 基于秩空间差异的多核组合方法[J]. 山东大学学报 (工学版), 2021, 51(1): 108-113.

WANG Mei, XUE Chenglong, ZHANG Qiang. Multi-kernel combination method based on rank spatial difference[J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 108-113.

参考文献

[1] 王文剑,田萌.核选择研究进展[J].山西大学学报(自然科学版),2017,40(3):460-471. WANG Wenjian, TIAN Meng. Advances in kernel selection research[J]. Journal of Shanxi University(Natural Science Edition), 2017, 40(3):460-471.
[2] SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.
[3] BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[4] KERM P V. Adaptive kernel density estimation[J]. Stata Journal, 2003, 3(2): 148-156.
[5] 李阳. 多核学习SVM算法研究及肺结节识别[D].长春:吉林大学,2014. LI Yang. Multiple kernel learning SVM and lung nodule recognition[D]. Changchun: Jilin University, 2014.
[6] SONNENBURG S, RATSCH G, SCHAFER C, et al. Large scale multiple kernel learning[J]. The Journal of Machine Learning Research, 2006, 7(7): 1531-1565.
[7] BACH F R. Consistency of the group Lasso and multiple kernel learning[J]. The Journal of Machine Learning Research, 2008, 9(6): 1179-1225.
[8] RAKOTOMAMONJY A, BACH F R, CANU S, et al. More efficiency in multiple kernel learning[C] //Proceedings of the 24th International Conference on Machine Learning. Corvalis, USA: ACM, 2007: 775-782.
[9] CORTES C, MOHRI M, ROSTAMIZADEH A. Learning sequence kernels[C] //Proceedings of the International Conference on Machine Learning for Signal Processing. Washington D. C., USA: IEEE, 2008:2-8.
[10] YANG Z, GUO J, XU W, et al. Multi-scale support vector machine for regression estimation[C] //Proceed-ings of the 3rd International Symposium on Neural Networks. Chengdu, China: Springer, 2006: 1030-1037.
[11] SONNENBURG S, RATSCH G, SCHAFER C. A general and efficient multiple kernel learning algorithm[C] //Proceedings of the Advances in Neural Infor-mation Processing Systems. Vancouver, Canada: The MIT Press, 2005:1273-1280.
[12] ZIEN A, ONG C S. Multiclass multiple kernel learning[C] //Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007: 1191-1198.
[13] LIU Yong, LIAO Shizhong, LIN Hailun, et al. Infinite kernel learning: generalization bounds and algorithms[C] //Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, USA, 2017: 2280-2286.
[14] LANCKRIET G R G, CRISTIANINI N, BARTLETT P, et al. Learning the kernel matrix with semidefinite programming[J]. The Journal of Machine Learning Research, 2004, 5(1): 27-72.
[15] LEE W J, VERZAKOV S, DUIN R P. Kernel com-bination versus classifier combination[C] // Proceedings of the 7th International Workshop on Multiple Classifier Systems. Prague,Czech Republic: Springer, 2007:22-31.
[16] 王梅,李董,孙莺萁,等.求解大规模问题的多核学习正则化路径算法[J].模式识别与人工智能,2018,31(2):190-196. WANG Mei, LI Dong, SUN Yingqi, et al. Regularization path algorithm of multiple kernel learning for solving large scale problems[J].Pattern Recognition and Artificial Intelligence, 2018, 31(2):190-196.
[17] 汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8):1037-1050. WANG Hongqiao, SUN Fuchun, CAI Yanning, et al. On multiple kernel learning methods[J]. Acta Automatica Sinica, 2010, 36(8):1037-1050.
[18] BENNETT K P, MOMMA M, EMBRECHTS M J. MARK: a boosting algorithm for heterogeneous kernel models[C] //Proceedings of 8th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton,Canada: ACM, 2002: 24-31.
[19] ONG C S, SMOLA A J, WILLIAMSON R C. Learning the kernel with hyperkernels[J]. The Journal of Machine Learning Research, 2005, 6(7): 1043-1071.
[20] RAKOTOMAMONJY A, BACH F R, CANU S, et al. Simple MKL[J]. The Journal of Machine Learning Research, 2008, 9(11): 2491-2521.
[21] 刘文婧,陈肖洁.多核LSSVM算法在轴承故障识别中的应用[J].机械设计与制造,2018(2):249-252. LI Wenjing, CHEN Xiaojie. Fault identification app-lication of rolling bearing based on LSSVM with multiple kernels[J]. Machinery Design & Manufacture, 2018(2):249-252.
[22] 王庆超,付光远,汪洪桥,等.基于局部空间变稀疏约束的多核学习方法[J].电子学报,2018,46(4):930-937. WANG Qingchao, FU Guangyuan, WANG Hongqiao, et al. Local variable sparsity based multiple kernel learning algorithm[J]. Acta Electronica Sinica, 2018, 46(4): 930-937.
[23] 陶剑文,王士同.多核局部领域适应学习[J].软件学报,2012,23(9):2297-2310. TAO Jianwen, WANG Shitong. Multiple kernel local learning-based domain adaptation[J]. Journal of Soft-ware, 2012, 23(9):2297-2310.
[24] 李飞,杜亮,任超宏.基于全局融合的多核概念分解算法[J].计算机应用,2019,39(4):1021-1026. LI Fei, DU Liang, REN Chaohong. Multiple kernel concept factorization algorithm based on global fusion[J]. Journal of Computer Applications, 2019, 39(4):1021-1026.
[25] 张庆朔,何强,张长伦,等.模糊多核一类支持向量机[J].北京建筑大学学报,2020,36(1):82-90. ZHANG Qingshuo, HE Qiang, ZHANG Changlun, et al. Fuzzy multiple kernel one-class support vector machine[J]. Journal of Beijing University of Civil Engineering and Architecture, 2020, 36(1):82-90.
[26] 罗林开. 支持向量机的核选择[D].厦门:厦门大学,2007. LUO Linkai. Research on Kernel Selection of Support Vector Machine[D]. Xiamen: Xiamen University, 2007.

相关文章 15

[1]	谢晓兰,王琦. 一种基于多目标的容器云任务调度算法[J]. 山东大学学报 (工学版), 2020, 50(4): 14-21.
[2]	蔡国永,贺歆灏,储阳阳. 基于空间注意力和卷积神经网络的视觉情感分析[J]. 山东大学学报 (工学版), 2020, 50(4): 8-13.
[3]	成科扬,孙爽,詹永照. 基于背景复杂度自适应距离阈值的修正SuBSENSE算法[J]. 山东大学学报 (工学版), 2020, 50(3): 38-44.
[4]	田枫,李欣,刘芳,李闯,孙小强,杜睿山. 基于多模态子空间学习的语义标签生成方法[J]. 山东大学学报 (工学版), 2020, 50(3): 31-37, 44.
[5]	马金平. 基于UART串口的多机通讯[J]. 山东大学学报 (工学版), 2020, 50(3): 24-30.
[6]	袁高腾,刘毅慧,黄伟,胡兵. 基于Gabor特征的乳腺肿瘤MR图像分类识别模型[J]. 山东大学学报 (工学版), 2020, 50(3): 15-23.
[7]	段江丽,胡新. 自然语言问答中的语义关系识别[J]. 山东大学学报 (工学版), 2020, 50(3): 1-7.
[8]	刘保成,朴燕,宋雪梅. 联合检测的自适应融合目标跟踪[J]. 山东大学学报 (工学版), 2020, 50(3): 51-57.
[9]	严云洋,杜晨锡,刘以安,高尚兵. 基于轻型卷积神经网络的火焰检测方法[J]. 山东大学学报 (工学版), 2020, 50(2): 100-107.
[10]	张胜男,王雷,常春红,郝本利. 基于三维剪切波变换和BM4D的图像去噪方法[J]. 山东大学学报 (工学版), 2020, 50(2): 83-90.
[11]	胡龙茂,胡学钢. 基于多维相似度和情感词扩充的相同产品特征识别[J]. 山东大学学报 (工学版), 2020, 50(2): 50-59.
[12]	陈艳平,冯丽,秦永彬,黄瑞章. 一种基于深度神经网络的句法要素识别方法[J]. 山东大学学报 (工学版), 2020, 50(2): 44-49.
[13]	闫威,张达敏,张绘娟,辛梓芸,陈忠云. 基于混合决策的改进鸟群算法[J]. 山东大学学报 (工学版), 2020, 50(2): 34-43.
[14]	宋士奇,朴燕,蒋泽新. 基于改进YOLOv3的复杂场景车辆分类与跟踪[J]. 山东大学学报 (工学版), 2020, 50(2): 27-33.
[15]	陈宁宁,赵建伟,周正华. 基于校正神经网络的视频追踪算法[J]. 山东大学学报 (工学版), 2020, 50(2): 17-26.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed