Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series Analysis
文献类型：期刊论文
| 作者 | Tang, Yunbo; Chen, Dan; Zuo, Yiping; Lu, Xiaoqiang ; Ranjan, Rajiv; Zomaya, Albert Y.; Yao, Quanming; Li, Xiaoli
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| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 35期号:4页码:3832-3845 |
| 关键词 | Electroencephalography Bayes methods Tensors Brain modeling Feature extraction Time series analysis Time-frequency analysis Multivariate time series Bayesian factorization sparsity prior structural feature construction |
| ISSN号 | 1041-4347;1558-2191 |
| DOI | 10.1109/TKDE.2021.3128770 |
| 产权排序 | 2 |
| 英文摘要 | Multivariate time series data (Mv-TSD) portray the evolving processes of the system(s) under examination in a multi-view manner. Factorization methods are salient for Mv-TSD analysis with the potentials of structural feature construction correlating various data attributes. However, research challenges remain in the derivation of factors dueto highly scattered data distribution of Mv-TSD and intensive interferences/outliers embedded in the source data. The proposed Enhanced Bayesian Factorization approach (Enhanced-BF) addresses the challenges in three phases: (1) variant scale partitioning applies to Mv-TSD according to degree of amplitude and obtains the blocks of variant scales; (2) hierarchical Bayesian model for tensor factorization automatically derives the factors of each block with interferences suppressed; (3) Bayesian unification model merges those block factors to construct the final structural features. Enhanced-BF has been evaluated using a case study of brain data engineering with multivariate electroencephalogram (EEG). Experimental results indicate that the proposed method manifests robustness to the interferences and outperforms the counterparts in terms of operation efficiency and error when factorizing EEG tensor. Besides, Enhanced-BFexcels in factorization-based analysis of ongoing autism spectrum disorder (ASD) EEG: 3 times speed-up in factorization and 87:35% accuracy in ASD discrimination. The latent factors (biomarkers) can distinctly interpret the typical EEG characteristics of ASD subjects. |
| 语种 | 英语 |
| WOS记录号 | WOS:000946283700041 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://ir.opt.ac.cn/handle/181661/96386]  |
| 专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Tang, Yunbo,Chen, Dan,Zuo, Yiping,et al. Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series Analysis[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(4):3832-3845. |
| APA | Tang, Yunbo.,Chen, Dan.,Zuo, Yiping.,Lu, Xiaoqiang.,Ranjan, Rajiv.,...&Li, Xiaoli.(2023).Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series Analysis.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(4),3832-3845. |
| MLA | Tang, Yunbo,et al."Enhanced Bayesian Factorization With Variant Scale Partitioning for Multivariate Time Series Analysis".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.4(2023):3832-3845. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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