Result: Data Processing Integrating Singular Value Decomposition Algorithm and Tensor Chain Decomposition Algorithm

Title:
Data Processing Integrating Singular Value Decomposition Algorithm and Tensor Chain Decomposition Algorithm
Authors:
Hao Zhang
Source:
IEEE Access, Vol 13, Pp 38964-38978 (2025)
Publisher Information:
Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year:
2025
Subject Terms:
tensor chain decomposition, parallel algorithm, Singular value decomposition, long short-term memory network, Electrical engineering. Electronics. Nuclear engineering, data processing, TK1-9971
Document Type:
Academic journal Article
ISSN:
2169-3536
DOI:
10.1109/access.2025.3546029
Access URL:
https://doaj.org/article/fb99359844de4b3ea5369e5190cb0b4d
Rights:
CC BY
Accession Number:
edsair.doi.dedup.....4a56b32fe0f95d8d57b14600f6e4c38a
Database:
OpenAIRE

Further Information

In the era of big data, processing information in multi-linear arrays is a challenge. In this paper, a parallel singular value decomposition algorithm based on unilateral Jacobi is proposed, and a data processing model of long and short memory network combined with the parallel tensor chain decomposition algorithm is constructed. The results show that the parallel efficiency of the algorithm reaches 0.95 with 30 cores, the compression ratio is 10, the accuracy and recall rate are 0.98 and 0.96, respectively. On the ImageNet dataset, model indicators are all over 0.9, showing excellent performance. The research not only improves the efficiency of data processing, but also provides new solutions for high-dimensional data analysis, especially in the aspects of feature extraction and dimensionality reduction. Combined with the advantages of LSTM in processing time series data, the overall performance of the model is improved.