Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing
Journal article
Hajderanj, L., Chen, D., Grisan, E. and Dudley-McEvoy, S (2020). Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing. IEEE Access. 8, pp. 207141 - 207155. https://doi.org/10.1109/ACCESS.2020.3038460
Authors | Hajderanj, L., Chen, D., Grisan, E. and Dudley-McEvoy, S |
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Abstract | In recent years, the huge expansion of digital technologies has vastly increased the volume of data to be explored, such that reducing the dimensionality of data is an essential step in data exploration. The integrity of a dimensionality reduction technique relates to the goodness of maintaining the data structure. Dimensionality reduction techniques such as Principal Component Analyses (PCA) and Multidimensional |
Keywords | dimensionality reduction; global structure; local structure; visualization; structure capturing |
Year | 2020 |
Journal | IEEE Access |
Journal citation | 8, pp. 207141 - 207155 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2020.3038460 |
Publication dates | |
17 Nov 2020 | |
Publication process dates | |
Accepted | 05 Nov 2020 |
Deposited | 05 Nov 2020 |
Publisher's version | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/8v13y
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