[1]郭瑛洁,王士同,许小龙.基于最大间隔理论的组合距离学习算法[J].智能系统学报编辑部,2015,10(6):843-850.[doi:10.11992/tis.201504027]
GUO Yingjie,WANG Shitong,XU Xiaolong.Learning a linear combination of distances based on the maximum-margin theory[J].CAAI Transactions on Intelligent Systems,2015,10(6):843-850.[doi:10.11992/tis.201504027]
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GUO Yingjie,WANG Shitong,XU Xiaolong.Learning a linear combination of distances based on the maximum-margin theory[J].CAAI Transactions on Intelligent Systems,2015,10(6):843-850.[doi:10.11992/tis.201504027]
基于最大间隔理论的组合距离学习算法
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第6期
页码:
843-850
栏目:
学术论文—人工智能基础
出版日期:
2015-12-25
- Title:
- Learning a linear combination of distances based on the maximum-margin theory
- Keywords:
- metric learning; hybrid distance metric; maximum-margin theory; fuzzy C-means; fuzzy clustering; clustering algorithm; metric; learning algorithm
- 分类号:
- TP181
- 摘要:
- 从已知数据集中学习距离度量在许多机器学习应用中都起着重要作用。传统的距离学习方法通常假定目标距离函数为马氏距离的形式,这使得学习出的距离度量在应用上具有局限性。提出了一种新的距离学习方法,将目标距离函数表示为若干候选距离的线性组合,依据最大间隔理论利用数据集的边信息学习得到组合距离中各距离分量的权值,从而得到新的距离度量。通过该距离度量在模糊C均值聚类算法中的表现来对其进行评价。在UCI数据集上,与其他已有的距离学习算法的对比实验结果证明了该文算法的有效性。
- Abstract:
- Learning a distance metric from given training samples is a crucial aspect of many machine learning tasks. Conventional distance metric learning approaches often assume the target distance function to be represented in the form of Mahalanobis distance, and the metric has limitations for this application. This paper proposes a new metric learning approach in which the target distance function is represented as a linear combination of several candidate distance metrics. This method obtains a new distance metric by learning weights from side information according to the maximum-margin theory. The new distance function is applied to fuzzy C-means clustering for evaluation. The experiments were performed using UCI data, and a comparison of the results with those of other approaches reveals the advantages of the proposed technique.
- 参考文献/References:
-
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[6] WEINBERGER K Q, BLITZER J C, SAUL L K. Distance metric learning for large margin nearest neighbor classification[C]//Advances in Neural Information Processing Systems. Cambridge, United Kingdom, 2006:1473-1480.
[7] 王骏, 王士同, 邓赵红. 特征加权距离与软子空间学习相结合的文本聚类新方法[J]. 计算机学报, 2012, 35(8):1655-1665. WANG Jun, WANG Shitong, DENG Zhaohong. A novel text clustering algorithm based on feature weighting distance and soft subspace learning[J]. Chinese Journal of Computers, 2012, 35(8):1655-1665.
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[9] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
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[12] WAGSTAFF K, CARDIE C, ROGERS S, et al. Constrained k-means clustering with background knowledge[C]//BAR-HILLEL A, HERTZ T, SHENTAL N, et al. Proceedings of the Eighteenth International Conference on Machine Learning. Williamstown, Australia,2001:577-584.
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- 相似文献/References:
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备注/Memo
收稿日期:2015-04-19;改回日期:。
基金项目:国家自然科学基金资助项目(61272210);江苏省自然科学基金资助项目(BK2011417,BK2011003).
作者简介:郭瑛洁,女,1991生,硕士研究生,主要研究方向为人工智能、模式识别。王士同,男,1964生,教授,博士生导师,主要研究方向为人工智能、模式识别和生物信息。许小龙,男,1989生,硕士研究生,主要研究方向为人工智能、模式识别。
通讯作者:郭瑛洁.E-mail:ying_dm@163.com.
更新日期/Last Update:
1900-01-01