Video-based human action and hand gesture recognition by fusing factored matrices of dual tensors

Hsieh, Chung-Yang; Lin, Wei-Yang

doi:10.1007/s11042-016-3407-1

Cited by 8 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tensor canonical correlation analysis (TCCA) was introduced by Kim et al 4,5 It was a generalization of CCA of multidimensional arrays (Tensor), which can be applied to motion/gesture classification in videos. Hsieh and Lin 6,7 introduced the method of calculating the similarity between tensors. The core idea was to consider each expansion matrix in the tensor as an individual to estimate the similarity between two tensors, which can effectively use each characteristic of the expansion matrix.…”

Section: Introductionmentioning

confidence: 99%

Tensor mutual information and its applications

Ren

Cui

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual-information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low-dimensional subspace.

show abstract

Section: Introductionmentioning

confidence: 99%