Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques

Seth Gyamerah; Godfred Tour Soori; Dennis Redeemer Korda; John Kwame Tawiah; Eric Ayintareba Akolgo; Emmanuel Oteng Dapaah

doi:doi:10.11648/j.ajece.20230702.12

Research Article |

Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques

Seth Gyamerah, Godfred Tour Soori, Dennis Redeemer Korda^*, John Kwame Tawiah, Eric Ayintareba Akolgo, Emmanuel Oteng Dapaah

Published in American Journal of Electrical and Computer Engineering (Volume 7, Issue 2)

Received: 29 October 2023 Accepted: 17 November 2023 Published: 11 December 2023

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Abstract

Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.

Published in	American Journal of Electrical and Computer Engineering (Volume 7, Issue 2)
DOI	10.11648/j.ajece.20230702.12
Page(s)	27-39
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Machine Learning, Principal Component Analysis, Linear Discriminant Analysis, Recursive Feature Elimination, Lasso Regression, Genomics, Medical Imaging

References

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[6]	L. Zhang, Z. Wang and Z. Liu, "A comparative study of dimensionality reduction techniques for cancer diagnosis," Journal of Biomedical Informatics, vol. 92, pp. 103-111, 2018.
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[8]	S. Ayesha, M. Kashif and R. Talib, "Overview and Comparative Study of Dimensionality Reduction Techniques for High Dimensional Data," Information Fusion, 2020.
[9]	V. Santhosh, " Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning," International Journal of Scientific Research in Science and, vol. 4, no. 8, pp. 364-369, 2018.
[10]	R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of Eugenics, vol. 7, no. 2, pp. 179-188, 1936.
[11]	M. Vikram, R. Pavan, N. D. Dineshbhai and B. Mohan, "Performance evaluation of dimensionality reduction techniques on high dimensional data," in 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019.
[12]	M. A. Belarbi, S. Mahmoudi, G. Belalem, S. A. Mahmoudi and A. Cools, "A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image," Big Data and Cognitive Computing, vol. 6, no. 2, 2022.
[13]	D. Mishra and S. Sharma, "Performance Analysis of Dimensionality Reduction Techniques: A Comprehensive Review," Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering, 2021.
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[16]	Wikipedia, "Principal component analysis," [Online]. Available: https://en.wikipedia.org/w/index.php?title=Principal_component_analysis&oldid=1168271511. [Accessed 3 August 2023].

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APA Style

Gyamerah, S., Tour Soori, G., Redeemer Korda, D., Kwame Tawiah, J., Ayintareba Akolgo, E., et al. (2023). Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. American Journal of Electrical and Computer Engineering, 7(2), 27-39. https://doi.org/10.11648/j.ajece.20230702.12

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ACS Style

Gyamerah, S.; Tour Soori, G.; Redeemer Korda, D.; Kwame Tawiah, J.; Ayintareba Akolgo, E., et al. Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. Am. J. Electr. Comput. Eng. 2023, 7(2), 27-39. doi: 10.11648/j.ajece.20230702.12

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AMA Style

Gyamerah S, Tour Soori G, Redeemer Korda D, Kwame Tawiah J, Ayintareba Akolgo E, et al. Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. Am J Electr Comput Eng. 2023;7(2):27-39. doi: 10.11648/j.ajece.20230702.12

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@article{10.11648/j.ajece.20230702.12,
  author = {Seth Gyamerah and Godfred Tour Soori and Dennis Redeemer Korda and John Kwame Tawiah and Eric Ayintareba Akolgo and Emmanuel Oteng Dapaah},
  title = {Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {7},
  number = {2},
  pages = {27-39},
  doi = {10.11648/j.ajece.20230702.12},
  url = {https://doi.org/10.11648/j.ajece.20230702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20230702.12},
  abstract = {Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.
},
 year = {2023}
}

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TY  - JOUR
T1  - Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques
AU  - Seth Gyamerah
AU  - Godfred Tour Soori
AU  - Dennis Redeemer Korda
AU  - John Kwame Tawiah
AU  - Eric Ayintareba Akolgo
AU  - Emmanuel Oteng Dapaah
Y1  - 2023/12/11
PY  - 2023
N1  - https://doi.org/10.11648/j.ajece.20230702.12
DO  - 10.11648/j.ajece.20230702.12
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 27
EP  - 39
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20230702.12
AB  - Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.

VL  - 7
IS  - 2
ER  -

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Author Information

Seth Gyamerah

Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana

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Godfred Tour Soori

Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana

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Dennis Redeemer Korda

Department of Information and Communication Technology, Bolgatanga Technical University, Bolgatanga, Ghana

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John Kwame Tawiah

Department of Civil Engineering, Ho Technical University, Ho, Ghana

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Eric Ayintareba Akolgo

Department of Computer Science, Regentropfen College of Applied Sciences, Bolgatanga, Ghana

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Emmanuel Oteng Dapaah

Department of Information and Communication Technology, E.P College of Education, Bimbila, Ghana

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Plain Text BibTeX RIS

APA Style

Gyamerah, S., Tour Soori, G., Redeemer Korda, D., Kwame Tawiah, J., Ayintareba Akolgo, E., et al. (2023). Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. American Journal of Electrical and Computer Engineering, 7(2), 27-39. https://doi.org/10.11648/j.ajece.20230702.12

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ACS Style

Gyamerah, S.; Tour Soori, G.; Redeemer Korda, D.; Kwame Tawiah, J.; Ayintareba Akolgo, E., et al. Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. Am. J. Electr. Comput. Eng. 2023, 7(2), 27-39. doi: 10.11648/j.ajece.20230702.12

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AMA Style

Gyamerah S, Tour Soori G, Redeemer Korda D, Kwame Tawiah J, Ayintareba Akolgo E, et al. Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques. Am J Electr Comput Eng. 2023;7(2):27-39. doi: 10.11648/j.ajece.20230702.12

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@article{10.11648/j.ajece.20230702.12,
  author = {Seth Gyamerah and Godfred Tour Soori and Dennis Redeemer Korda and John Kwame Tawiah and Eric Ayintareba Akolgo and Emmanuel Oteng Dapaah},
  title = {Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {7},
  number = {2},
  pages = {27-39},
  doi = {10.11648/j.ajece.20230702.12},
  url = {https://doi.org/10.11648/j.ajece.20230702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20230702.12},
  abstract = {Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Comparative Analysis of Feature Extraction of High Dimensional Data Reduction Using Machine Learning Techniques
AU  - Seth Gyamerah
AU  - Godfred Tour Soori
AU  - Dennis Redeemer Korda
AU  - John Kwame Tawiah
AU  - Eric Ayintareba Akolgo
AU  - Emmanuel Oteng Dapaah
Y1  - 2023/12/11
PY  - 2023
N1  - https://doi.org/10.11648/j.ajece.20230702.12
DO  - 10.11648/j.ajece.20230702.12
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 27
EP  - 39
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20230702.12
AB  - Dimensionality reduction is critical for analyzing and interpreting high-dimensional data across domains like genomics, imaging, and finance. This paper presents a comparative analysis of dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Recursive Feature Elimination (RFE), and Lasso regression. These methods are applied to datasets from genomics, medical imaging, and finance to evaluate their ability to reduce dimensions while preserving relevant information. The results demonstrate that PCA and LDA are highly effective for genomics data, reducing gene expression profiles from over 60,000 dimensions to 10-50 components while maintaining precision of over 80%. For medical images, PCA and LDA reduce pixel dimensions by over 90% without compromising precision. However, no single technique optimizes dimensionality reduction and precision for complex finance data. Overall, the analysis provides domain-specific insights, highlighting PCA and LDA as leading techniques for genomics and imaging. The choice of method should be guided by data characteristics. Testing on more diverse, real-world datasets is needed to establish validity further. This research aims to inform the selection of appropriate data reduction techniques across critical applications involving high-dimensional data.

VL  - 7
IS  - 2
ER  -

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