Linear dimensionality reduction method based on topological properties

Yao, Yuqin; Meng, Hua; Gao, Yang; Long, Zhiguo; Li, Tianrui

doi:10.1016/j.ins.2022.12.098

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The t-SNE algorithm improves the computational efficiency compared to the SNE algorithm, but it has no unique optimal solution and cannot be used for prediction. Currently, the corresponding mainstream linear variants of these three common DR algorithms are LPP (Yao et al, 2023), NPE (Zhang et al, 2023), and t-SNLE (Xia et al, 2021). They alleviate the curse of dimensionality and retain the advantages of cluster separation, but it is difficult to maintain the topological connectivity of the data after dimensionality reduction (Yao et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Cui,

Xu,

2023

Journal of Data and Information Science

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Purpose Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance. Additionally, the interpretability of these models presents a persistent challenge. Design/methodology/approach This paper proposes two innovative dimensionality reduction models based on integer programming (DRMBIP). These models assess compactness through the correlation of each indicator with its class center, while separation is evaluated by the correlation between different class centers. In contrast to DRMBIP-p, the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation. Findings This study, getting data from the Global Health Observatory (GHO), investigates 141 indicators that influence life expectancy. The findings reveal that DRMBIP-p effectively reduces the dimensionality of data, ensuring compactness. It also maintains compatibility with other models. Additionally, DRMBIP-v finds the optimal result, showing exceptional separation. Visualization of the results reveals that all classes have a high compactness. Research limitations The DRMBIP-p requires the input of the correlation threshold parameter, which plays a pivotal role in the effectiveness of the final dimensionality reduction results. In the DRMBIP-v, modifying the threshold parameter to variable potentially emphasizes either separation or compactness. This necessitates an artificial adjustment to the overflow component within the objective function. Practical implications The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators. Validated by life expectancy data, this paper demonstrates potential to assist data miners with the reduction of data dimensions. Originality/value To our knowledge, this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering. It not only has applications in life expectancy, but also has obvious advantages in data mining work that requires precise class centers.

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