Speed Up Kernel Projection Vector Machine Using Kronecker Decomposition

机译：使用Kronecker分解加速内核投影矢量机

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We present a speedup algorithm for kernel projection vector machine (KPVM) based on kronecker product decomposition. The large scale kernel matrix K with size of n × n is factorized into two small matrices K1 and K2 with size n1 × n1 and n2 × n2 respectively where n1 × n2 = n. The time-consuming SVD operation on K in KPVM is calculated through K1 and K2. The computation complexity is reduced to O(n²} from O(n³) originally while generalization ability is undiminished or even better than KPVM.

机译：我们提出了一种基于kronecker积分解的核投影向量机（KPVM）加速算法。大小为n×n的大规模核矩阵K被分解为两个大小为n 1 ×n的小矩阵K 1 和K 2 1 和n 2 ×n 2 ，其中n 1 ×n 2 = 。通过K 1 和K 2 计算KPVM中对K进行的耗时的SVD操作。原本的运算复杂度从O（n ^{3 ）降低到O（n ^{2 }，而泛化能力却没有下降，甚至优于KPVM。}}

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来源
《2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining》|2012年|722-725|共4页
会议地点 Taipei(CT)
作者
Deng WanYu; Kai Zhang; QingHua Zheng;
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作者单位

School of Computer, Xi'an University of PostTelecommunications, Chinac;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Kronecker product; Neural network; Projection vector machine;

机译：Kronecker产品；神经网络；投影向量机；;

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Speed Up Kernel Projection Vector Machine Using Kronecker Decomposition

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