Tensor local linear embedding with global subspace projection optimisation

IET Computer Vision

Guo Niu¹  Zhengming Ma² 

[1] School of Electronics and Information Technology Foshan University Foshan China;School of Electronics and Information Technology Sun Yat‐sen University Guangzhou China;

关键词: local linear embedding;    subspace projection;    tensor dimensionality reduction;    tensors;   

DOI  :  10.1049/cvi2.12083

来源: DOAJ

【 摘 要 】

Abstract In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. Firstly, we analyse the local linear feature of tensor data for learning the linear separable embedding of the tenor data. Furthermore, a global subspace projection distance minimisation strategy is introduced to extract the global characteristic of the tensor data. The aim of this strategy is to find an optimal low‐dimensional subspace for TDR. In particular, two novel TDR algorithms are developed by the ensemble of tensor local feature preservation and global subspace projection distance minimisation, which express the subspace projection optimisation as an iteration optimisation problem and a Rayleigh quotient problem, respectively. The extensive experimental results on tensor data classification and clustering have demonstrated the proposed algorithms performed well.

【 授权许可】

Unknown