FAST KERNEL DISTRIBUTION FUNCTION ESTIMATION AND FAST KERNEL DENSITY ESTIMATION BASED ON SPARSE BAYESIAN LEARNING AND REGULARIZATION
In this paper, we develop a novel method of obtaining very sparse representation of Kernel Distribution Function Estimation (KDFE) and Kernel Density Estimation (KDE) exploiting Sparse Bayesian Regression (SBR) technique with the aidance of regularization by jittering. SBR introduces a parameterized sparsity-inducing prior on the unknown parameters of the linear model. After reviewing the existent methodologies of fast kernel density estimation, we adapt SBR to the problem of construction of sparse KDFE and KDE. Numerical results of preliminary simulation studies on synthetic data demonstrate the effectiveness of our algorithm which can achieve sparser representation of KDE than SVM-based algorithm and can produce more precise estimate than traditional full-sample KDE algorithm.
Fast Kernel Density Estimation Sparse Bayesian Learning Mean Integrated Squared Error III-posed problem Regularization Jittering Relevance Vector
XUN-FU YIN ZHI-FENG HAO
College of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, School of Mathematical Science, South China University of Technology, Guangzhou 510640, P.R.China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1756-1761
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）