| 摘要: |
| 提出了一种基于DI-FCM(double indices fuzzy C-means)算法框架的无监督距离学习算法——基于混合距离学习的双指数模糊C均值算法HDDI-FCM(double indices fuzzy C-means with hybrid distance).数据集未知距离度量被表示为若干已有距离的线性组合,然后执行HDDI-FCM,在对数据集进行有效聚类的同时进行距离学习.为了保证迭代算法收敛,引入了Steffensen迭代法来改进计算簇中心点的迭代公式.讨论了算法中参数的选择.基于UCI(University of California,Irvine)数据集的实验结果表明该算法是有效的. |
| 关键词: 距离学习 聚类 模糊C均值算法 混合距离 Steffensen迭代法 |
| DOI： |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60773206, 60704047, 90820002 (国家自然科学基金) |
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| Double Indices FCM Algorithm Based on Hybrid Distance Metric Learning |
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WANG Jun,WANG Shi-Tong
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| Abstract: |
| To learn a good distance metric without any class label information, an algorithm named HDDI-FCM (double indices fuzzy C-means with hybrid distance) is proposed in this paper. In detail, the unknown distance metric is firstly represented as the linear combination of several known distance metrics. Then the algorithm is executed to perform the clustering task as well as learn the most suitable metric simultaneously. To guarantee the convergence of the algorithm, the Steffensen iteration is introduced into the process of updating cluster centers. The selection of parameter for the algorithm is also discussed. The experimental results on a collection of UCI (University of California, Irvine) datasets demonstrate the effectiveness of the proposed algorithm. |
| Key words: distance metric learning clustering fuzzy C-means algorithm hybrid distance metric Steffensen iteration method |
