Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving
Journal article
Hajderanj, L., Chen, D. and Dudley-Mcevoy, S. (2023). Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving. Information Sciences. 661, p. 120030. https://doi.org/10.1016/j.ins.2023.120030
Authors | Hajderanj, L., Chen, D. and Dudley-Mcevoy, S. |
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Abstract | As an effective dimensionality reduction method, Same Degree Distribution (SDD) has been demonstrated to be able to maintain better data structure than other dimensionality reduction methods. Hence, tuning the degree of degree-distribution makes SDD a less costly method than other methods that require tuning the number of neighbours or perplexity. Although these advantages, SDD is still an expensive method compared with parameter-free methods such as PCA and MDS. A parameter-free SDD is proposed based on standard SDD, but it has two main differences: 1) it does not require tuning the degree of degree-distribution in the entire range from 1 to 15, but only uses degree 1; and 2) it re-scales the pairwise distances in the range [0, 2] instead of range [0, 1]. A theoretical analysis is presented to prove the better performance of parameter-free SDD. This paper also proposes a parametric version of SDD using a deep neural network approach to learn the mapping based on the samples of the original data and their corresponding embedded representations in a low dimensional space. Comparative experiments have been undertaken with SDD and other methods to demonstrate the effectiveness of the parametric SDD. |
Keywords | high dimensional data; dimensionality reduction techniques; structure capturing; computational time |
Year | 2023 |
Journal | Information Sciences |
Journal citation | 661, p. 120030 |
Publisher | Elsevier |
ISSN | 0020-0255 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2023.120030 |
Publication dates | |
22 Dec 2023 | |
Publication process dates | |
Accepted | 20 Dec 2023 |
Deposited | 21 Dec 2023 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/95y51
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Accepted author manuscript
INS-D-21-4304_R1 2023 12 20 1.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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