Local Adaptive and Incremental Gaussian Mixture for Online Density Estimation

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• 作者：Tianyu Qiu (10)
  Furao Shen (10)
  Jinxi Zhao (10)
  
  10. National Key Laboratory for Novel Software Technology ; Nanjing University ; Nanjing ; 210093 ; China
  
• 关键词：Online density estimation ; Gaussian mixture ; Local adaptive ; Incremental learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9077
• 期：1
• 页码：418-428
• 全文大小：265 KB
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  17. Chang, C-C, Lin, C-J (2011) LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2: pp. 27
  
• 作者单位：Advances in Knowledge Discovery and Data Mining
• 丛书名：978-3-319-18037-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper, we propose an incremental and local adaptive gaussian mixture for online density estimation (LAIM). Using a similarity threshold based criterion, the method is able to allocate components incrementally to accommodate novel data points without affecting previously learned components. A local adaptive learning strategy is presented for estimating density with complex structure in an online way. We also adopt a denoising scheme to make the algorithm more robust to noise. We compared the LAIM to the state-of-art methods for density estimation in both artificial and real data sets, the results show that our method outperforms the compared online counterpart and produces comparable results to the compared batch algorithms.