# Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy

arXiv: https://arxiv.org/abs/2005.04605 · HTML: https://mlsanity.com/paper/2005.04605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## Built on by (most cited first, 4 of 4)

- [Tensor PCA from basis in tensor space](https://mlsanity.com/paper/2305.02803/) (2023)
- [Decomposition of linear tensor transformations](https://mlsanity.com/paper/2309.07819/) (2023)
- [Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving](https://mlsanity.com/paper/2506.23523/) (2025)
- [FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel](https://mlsanity.com/paper/2610.04212/) (2026)

## Abstract (arXiv)

Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.