Cyganek, B. (2015) Visual Pattern Recognition Framework Based on the Best Rank Tensor Decomposition. In: Tavares, J.M.R.S. and Jorge, R.N., Eds., Developments in Medical Image Processing and Computational Vision, Springer International Publishing, Berlin, 89-103.

has been cited by the following article:

• TITLE: Tensor Robust Principal Component Analysis via Hybrid Truncation Norm

  AUTHORS: Yujie Luan, Wei Jiang

  KEYWORDS: Tensor Robust Principal Component Analysis (TRPCA), Hybrid Truncation Norm, Alternating Direction Multiplier Method (ADMM)

  JOURNAL NAME: Open Access Library Journal, Vol.9 No.10, October 24, 2022

  ABSTRACT: This paper mainly studies the problem of tensor robust principal component analysis (TRPCA), in order to accurately recover the low rank and sparse components from the observed signals. Most of the existing robust principal component analysis (RPCA) methods are based on nuclear norm minimizationss. These methods minimize all singular values at the same time, so they can not approach the rank well in practice. In this paper, the idea of truncated nuclear norm regularization is extended to RPCA. At the same time, in order to improve the stability of the model, the tensor truncated Frobenius norm is newly defined. Truncated nuclear norm and truncated Frobenius norm are considered at the same time called hybrid truncated model of tensor. This method minimizes min singular values. In addition, this paper also gives an effective method to determine the contraction operator, and develops an effective iterative algorithm based on alternating direction to solve this optimization problem. Experimental results show the effectiveness and accuracy of this method.