A new canonical polyadic decomposition algorithm with improved stability and its applications to biomedical signal processing

作者：Xie, Kan; Yu, Jinshi^*; Lu, Chuan

来源：Cluster Computing, 2017, 20(2): 1449-1455.

DOI：10.1007/s10586-017-0858-8

• Tensor decompositions
• Canonical polyadic decomposition
• Biomedical signal processing
• Blind source separation

In biomedical engineering applications, high-order data, also known as tensors, are more and more popular and canonical polyadic decomposition (CPD) is one of the most powerful tool to analyze such high-dimensional high-order data. However, existing CPD algorithms suffer from a serious disadvantage: they are prone to stick into local minima and hence may result in unreasonable components that are hard to interpret. To overcome this problem, we proposed a new CPD algorithm that not only provides significantly improved stability but is also very suitable for parallel computing. The performance of the proposed algorithm was justified by using synthetic data and its applications in biomedical image denoising.

• 出版日期2017-6
• 单位广东工业大学