Tensor decomposition for infectious disease incidence data

Korevaar, Hannah; Metcalf, C. Jessica E.; Grenfell, Bryan T.

doi:10.1111/2041-210x.13480

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…This work mainly studies the incremental decomposition method of tensor chain. In the process of decomposing a tensor, first, a specific decomposition algorithm is required to carry out on the original tensor and decompose it into a core tensor and multiple adjoint matrices [16][17] . Such methods mainly include typical multidimensional decomposition (CPC), Tucker decomposition and higher-order singular value decomposition [18][19] .…”

Section: Transformation Methodsmentioning

confidence: 99%

Data distribution and tensor influence analysis of different clustering methods

Zhang

Hao

et al. 2023

Preprint

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At present, people are in the era of big data, which is changing people's views of the world. However, it has the characteristics of various types, huge scale, and complex relationships. In order to solve the repeated calculation caused by streaming data in the processing of tensor-based big data, there will also be dimension disasters. Therefore, in this paper, an incremental tensor train decomposition (ITTD) method is proposed to solve multi-clustering problem in tensor-based big data analysis systems. It mainly uses results of the tensor train decomposition obtained from the original tensor to calculate and updates the results of tensor train decomposition to avoid the repetitive decomposition of the original tensor and enhance the decomposition efficiency. The performance of ITTD method is tested through theoretical analysis, a large number of simulation data and a comparative experiment on the real data of public transportation in a region. The experimental results indicate that the execution time of ITTD is significantly shorter than that of nonincremental tensor train decomposition(NTTD) with time. However, as time goes by, there is no obvious difference in the approximation error and storage space between the two.. This shows that, compared to that of the traditional nonincremental method, if the approximation error and storage space are close, the execution time of the incremental method will be greatly shortened. It can improve the processing efficiency of multi-clustering problems in the tensor-based big data analysis system.

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Section: Transformation Methodsmentioning

confidence: 99%

Data distribution and tensor influence analysis of different clustering methods

Zhang

Hao

et al. 2023

Preprint

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“…Tensor decomposition 20,21 has been increasingly applied for solving challenging problems in medicine and health over recent years [22][23][24][25][26][27][28] . In cancer, tensor decomposition has recently been applied for identifying microRNA (miRNA) and mRNA expression profiles as potential prognostic biomarkers for kidney renal clear cell carcinoma, where genes involving in cancerrelated pathways were found to be associated with miRNAs 29 .…”

Section: Introductionmentioning

confidence: 99%

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy

Pham

Ravi

Fan

et al. 2023

The American Journal of Pathology

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“…This can be achieved using wavelet fitting and tensor decomposition methods assisted by ML/DL. [14][15][16][17] However, there are mathematically simpler and more transparent ways to extract useful additional information from univariate time series. For example, the trend-attribute analysis method extracts multiple variables from near-past information from univariate time trends.…”

Section: Introductionmentioning

confidence: 99%

Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence

Wood¹

2023

Infect Dis Immun

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Background “Chickenpox” is a highly infectious disease caused by the varicella-zoster virus, influenced by seasonal and spatial factors. Dealing with varicella-zoster epidemics can be a substantial drain on health-authority resources. Methods that improve the ability to locally predict case numbers from time-series data sets every week are therefore worth developing. Methods Simple-to-extract trend attributes from published univariate weekly case-number univariate data sets were used to generate multivariate data for Hungary covering 10 years. That attribute-enhanced data set was assessed by machine learning (ML) and deep learning (DL) models to generate weekly case forecasts from next week (t0) to 12 weeks forward (t+12). The ML and DL predictions were compared with those generated by multilinear regression and univariate prediction methods. Results Support vector regression generates the best predictions for weeks t0 and t+1, whereas extreme gradient boosting generates the best predictions for weeks t+3 to t+12. Long-short-term memory only provides comparable prediction accuracy to the ML models for week t+12. Multi–K-fold cross validation reveals that overall the lowest prediction uncertainty is associated with the tree-ensemble ML models. Conclusion The novel trend-attribute method offers the potential to reduce prediction errors and improve transparency for chickenpox time series.

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Tensor decomposition for infectious disease incidence data

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 40 publications

Data distribution and tensor influence analysis of different clustering methods

Data distribution and tensor influence analysis of different clustering methods

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy

Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence

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