Robust sparse kernel density estimation by inducing randomness

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• 作者：Fei Chen (1) (3)
  Huimin Yu (1) (2)
  Jincao Yao (1)
  Roland Hu (1)
  
  1. Department of Information Science and Electronic Engineering ; Zhejiang University ; No. 38 Zheda Road ; Hangzhou ; 310027 ; China
  3. School of Sciences ; Jimei University ; Xiamen ; 361021 ; China
  2. State Key Laboratory of CAD&CG ; Hangzhou ; 310027 ; China
  
• 关键词：Kernel density estimator ; Reduced set density estimator ; Integrated squared error ; Sequential minimal optimization ; Randomness
• 刊名：Pattern Analysis & Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：18
• 期：2
• 页码：367-375
• 全文大小：833 KB
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• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  
• 出版者：Springer London
• ISSN：1433-755X

文摘

In this paper, a robust sparse kernel density estimation based on the reduced set density estimator is proposed. The key idea is to induce randomness to the plug-in estimation of weighting coefficients. The random fluctuations can inhibit these small nonzero weighting coefficients to cluster in regions of space with greater probability mass. By sequential minimal optimization, these coefficients are merged into a few larger weighting coefficients. Experimental studies show that the proposed model is superior to several related methods both in sparsity and accuracy of the estimation. Moreover, the proposed density estimation is extensively validated on novelty detection and binary classification.