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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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基于多线性主成分分析的支持高阶张量机
DOI: 10.13232/j.cnki.jnju.2014.02.014
Keywords: 支持高阶张量机多线性主成分分析张量分解交替投影张量机
Abstract:
为了传统的学习算法张量向量破坏原始数据固有的高阶结构和内在相关性，信息丢失产生高维向量，使得后期学习过程中容易出现过拟合、维度灾难和小样本问题。近年考虑到张量的高维性和高冗余性，提出基于多线性主成分分析的支持高阶张量机分类算法(multilinearprinciplecomponentanalysisasedsupporthigh-ordertensormachine,mpca+shtm)。该算法首先利用多线性主成分分析对张量进行降维，然后利用支持高阶张量机对降维后的张量进行学习。在12个张量数据集上的实验表明：mpca+shtm在保持测试精度的情况下有效地降低了shtm的计算时间。
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