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Study on Computing the Support Vectors of Massive Data Based on Neighborhood Principle

ZHANG Wen sheng,\ DING Hui,\ WANG Jue(Institute of Automation, The Chinese Academy of Sciences, Beijing 100080, China)  
It is quite difficult to compute the support vectors of massive data using the theory of support vector machine. To solve this problem, a method is brought forward to compute support vectors based on the neighborhood principle in this paper. Several questions are discussed based upon comparison and analysis of the support vector machine theory and the neighborhood principle as below: (1) The inner product function from the sample space to the dimension expand space via the feature space is constructed, and the neighborhood principle of computing the support vectors is presented; (2) Vapnik's support vector machine theory is constructed on the distance space, the algorithm is designed to compute support vectors, and the algorithm is regarded as a method to reduce the computation of quadratic programming; (3) The experimental results show that the neighborhood principle can solve the problem of support vector computation of massive data effectively.
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