华东交通大学学报2018，Vol.35Issue(2)：120-128,9.
张量分解算法研究与应用综述
Research and Application of Tensor Decomposition Algorithm
摘要
Abstract
Tensor decomposition is a significant method to deal with large-scale data, which can reduce the data effectively.The high-order tensor is widely used in neuroscience,signal processing,image analysis,computer vi-sion and other fields as it has such advantages as uniqueness, robustness to noises and zero impact on the origi-nal data of the spatial structure and internal potential information. In this paper, the traditional dimensionality reduction methods were introduced firstly, and their problems and shortcomings were also discussed. Secondly, general analysis of three classical algorithms of tensor decomposition was carried out from the aspects of algo-rithm, basic ideas, algorithm framework and algorithm applications of CP decomposition, Tucker decomposition and non-negative tensor decomposition. Then, The CP decomposition algorithm and the Tucker decomposition algorithm were compared and analyzed from different angles. Finally, the present situation, practical application and future research trends of tensor decomposition were summarized and analyzed.关键词
张量/CP分解/Tucker分解/非负张量分解Key words
tensor/CP decomposition/tucker decomposition/non-negative tensor decomposition分类
信息技术与安全科学引用本文复制引用
熊李艳,何雄,黄晓辉,黄卫春..张量分解算法研究与应用综述[J].华东交通大学学报,2018,35(2):120-128,9.基金项目
国家自然科学基金项目(61363072,61462027,6156202) （61363072,61462027,6156202）
江西省研究生创新基金(YC2016-S261) （YC2016-S261）
江西省自然科学基金项目(2016BAB212050) （2016BAB212050）
江西省科技成果转移转化计划项目(20161BB190032,20142BB190027) （20161BB190032,20142BB190027）
