A Metric Learning Based Approach to Evaluate Task-Specific Time Series Similarity

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• 作者：Yang Lu (21)
  Wayne Xin Zhao (21)
  Hongfei Yan (21)
  Xiaoming Li (21)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7923
• 期：1
• 页码：326-331
• 全文大小：318KB
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• 作者单位：Yang Lu (21)
  Wayne Xin Zhao (21)
  Hongfei Yan (21)
  Xiaoming Li (21)
  
  21. Peking University, Beijing, China
  
• ISSN：1611-3349

文摘

A variety of methods have been proposed to measure time series similarity, such as Dynamic Time Warping and Edit distance. Although these methods have been shown to be effective and useful in various data mining tasks, they seldom consider task-specific information. Without consideration of task-specific information, the similarity measures may not work quite well on specific tasks. In this paper, we investigate how to learn task-specific time series similarity measures. We adopt metric learning as the principled approach, and we proposed two novel models based on metric learning to evaluate task-specified time series similarity. We construct our test collection based on real data from Renren Games data. Extensive experimental results show that our proposed methods are very effective.