Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition.

IEEE Transactions on Neural Networks and Learning Systems(2016)

引用 62|浏览66
暂无评分
摘要
Low-rank tensor completion (LRTC) has successfully been applied to a wide range of real-world problems. Despite the broad, successful applications, existing LRTC methods may become very slow or even not applicable for large-scale problems. To address this issue, a novel core tensor trace-norm minimization (CTNM) method is proposed for simultaneous tensor learning and decomposition, and has a much ...
更多
查看译文
关键词
Tensile stress,Computational modeling,Minimization,Matrix decomposition,Computational efficiency,Learning systems,Computational complexity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要