.png){kind=icon}
返回
Robust CP Tensor Factorization With Skew Noise
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2020.2991581.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The low-rank tensor factorization (LRTF) technique has received increasing popularity in data science, especially in computer vision applications. Many robust LRTF models have been presented recently. However, none of them take the skewness of data into account. This letter proposes a novel LRTF model for skew data analysis by modeling noise as a Mixture of Asymmetric Laplacians (MoAL). The numerical experiments show that the new model MoAL-LRTF outperforms several state-of-the-art counterparts. The codes for all the experiments are available at https://xsxjtu.github.io/Projects/MoAL/main.html.
Keyword:
Tensor factorization
mixture of asymmetric Laplacians
expectation-maximization (EM) algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects多模态数据融合: 方法、挑战与展望综述.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians高斯混合噪声建模的鲁棒张量分解广义模型
PREAI
分享收藏
查看更多内容