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Title:
Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets
Author: Wang, Shitong ; Wang, Jun ; Chung, Fu-lai
Keyword: Kernel density estimate (KDE) ; kernel methods ; quadratic programming (QP) ; sampling ; support vector machine (SVM)
Source: IEEE TRANSACTIONS ON CYBERNETICS
Issued Date: 2014
Volume: 44, Issue:1, Pages:1-20
Indexed Type: SCI
Department: [Wang, Shitong; Wang, Jun] Jiangnan Univ, Sch Digital Media, Wuxi 214122, Peoples R China. [Wang, Shitong; Chung, Fu-lai] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. [Wang, Shitong] Chinese Acad Sci, Inst Software, Natl Key Lab Comp Sci, Beijing 100080, Peoples R China.
Abstract: Kernel methods such as the standard support vector machine and support vector regression trainings take O(N-3) time and O(N-2) space complexities in their naive implementations, where N is the training set size. It is thus computationally infeasible in applying them to large data sets, and a replacement of the naive method for finding the quadratic programming (QP) solutions is highly desirable. By observing that many kernel methods can be linked up with kernel density estimate (KDE) which can be efficiently implemented by some approximation techniques, a new learning method called fast KDE (FastKDE) is proposed to scale up kernel methods. It is based on establishing a connection between KDE and the QP problems formulated for kernel methods using an entropy-based integrated-squared-error criterion. As a result, FastKDE approximation methods can be applied to solve these QP problems. In this paper, the latest advance in fast data reduction via KDE is exploited. With just a simple sampling strategy, the resulted FastKDE method can be used to scale up various kernel methods with a theoretical guarantee that their performance does not degrade a lot. It has a time complexity of O(m(3)) where m is the number of the data points sampled from the training set. Experiments on different benchmarking data sets demonstrate that the proposed method has comparable performance with the state-of-art method and it is effective for a wide range of kernel methods to achieve fast learning in large data sets.
English Abstract: Kernel methods such as the standard support vector machine and support vector regression trainings take O(N-3) time and O(N-2) space complexities in their naive implementations, where N is the training set size. It is thus computationally infeasible in applying them to large data sets, and a replacement of the naive method for finding the quadratic programming (QP) solutions is highly desirable. By observing that many kernel methods can be linked up with kernel density estimate (KDE) which can be efficiently implemented by some approximation techniques, a new learning method called fast KDE (FastKDE) is proposed to scale up kernel methods. It is based on establishing a connection between KDE and the QP problems formulated for kernel methods using an entropy-based integrated-squared-error criterion. As a result, FastKDE approximation methods can be applied to solve these QP problems. In this paper, the latest advance in fast data reduction via KDE is exploited. With just a simple sampling strategy, the resulted FastKDE method can be used to scale up various kernel methods with a theoretical guarantee that their performance does not degrade a lot. It has a time complexity of O(m(3)) where m is the number of the data points sampled from the training set. Experiments on different benchmarking data sets demonstrate that the proposed method has comparable performance with the state-of-art method and it is effective for a wide range of kernel methods to achieve fast learning in large data sets.
Language: 英语
WOS ID: WOS:000328948900001
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16891
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Wang, Shitong,Wang, Jun,Chung, Fu-lai. Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014-01-01,44(1):1-20.
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