Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) for Effective Dimensionality Reduction

Seyed-Ali Sadegh-Zadeh; Nasrin Sadeghzadeh; Saeed Shiry Ghidary; Sobhan Movahedi; Kaveh Kavianpour; Mohammad Amin Barati; Alireza Soleimani Mamalo

doi:10.62762/FBSP.2025.800185

Volume 1, Issue 2, Frontiers in Biomedical Signal Processing

Volume 1, Issue 2, 2026

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Frontiers in Biomedical Signal Processing, Volume 1, Issue 2, 2026: 79-104

Open Access | Research Article | 02 March 2026

Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) for Effective Dimensionality Reduction

Seyed-Ali Sadegh-Zadeh 1

Nasrin Sadeghzadeh 2

Saeed Shiry Ghidary 1

Sobhan Movahedi 3

Kaveh Kavianpour 4

Mohammad Amin Barati 5

Alireza Soleimani Mamalo 6 *

1 Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, United Kingdom

2 Department of Mathematics, Faculty of Basic Sciences, University of Qom, Qom 37161-46611, Iran

3 Islamic Azad University, Science and Research, Tehran, Iran

4 Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 15875-4413, Iran

5 Department of Mechanical Engineering, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 14179-35840, Iran

6 Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran

* Corresponding Author: Alireza Soleimani Mamalo, [email protected]

DOI: 10.62762/FBSP.2025.800185

ARK: ark:/57805/fbsp.2025.800185

Received: 31 March 2025, Accepted: 07 July 2025, Published: 02 March 2026

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Abstract

The Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) algorithm is introduced as a novel dimensionality reduction technique that integrates manifold learning with autoencoders to capture the intrinsic geometry of high-dimensional data effectively. The study evaluates the impact of various adjustments and enhancements within the "Manifold Adjustment Box," including different regularization techniques, activation functions, and architectural choices, across diverse datasets. Key findings demonstrate that configurations such as Leaky ReLU Activation and Batch Norm Layer consistently improve accuracy, results highlight the flexibility and robustness of AMRAE. The results underscore the significance of AMRAE in addressing the limitations of traditional dimensionality reduction methods, with potential applications in various fields requiring high-dimensional data analysis. This paper provides a comprehensive summary of the objectives, methodology, results, and conclusions, showcasing AMRAE's efficacy in improving data representation and machine learning performance.

Graphical Abstract

Keywords

dimensionality reduction

manifold concept regularized autoencoders

machine learning

high-dimensional data

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Sadegh-Zadeh, S. A., Sadeghzadeh, N., Ghidary, S. S., Movahedi, S., Kavianpour, K., Barati, M. A., & Mamalo, A. S. (2026). Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) for Effective Dimensionality Reduction. Frontiers in Biomedical Signal Processing, 1(2), 79–104. https://doi.org/10.62762/FBSP.2025.800185

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TY  - JOUR
AU  - Sadegh-Zadeh, Seyed-Ali
AU  - Sadeghzadeh, Nasrin
AU  - Ghidary, Saeed Shiry
AU  - Movahedi, Sobhan
AU  - Kavianpour, Kaveh
AU  - Barati, Mohammad Amin
AU  - Mamalo, Alireza Soleimani
PY  - 2026
DA  - 2026/03/02
TI  - Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) for Effective Dimensionality Reduction
JO  - Frontiers in Biomedical Signal Processing
T2  - Frontiers in Biomedical Signal Processing
JF  - Frontiers in Biomedical Signal Processing
VL  - 1
IS  - 2
SP  - 79
EP  - 104
DO  - 10.62762/FBSP.2025.800185
UR  - https://www.icck.org/article/abs/FBSP.2025.800185
KW  - dimensionality reduction
KW  - manifold concept regularized autoencoders
KW  - machine learning
KW  - high-dimensional data
AB  - The Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) algorithm is introduced as a novel dimensionality reduction technique that integrates manifold learning with autoencoders to capture the intrinsic geometry of high-dimensional data effectively. The study evaluates the impact of various adjustments and enhancements within the "Manifold Adjustment Box," including different regularization techniques, activation functions, and architectural choices, across diverse datasets. Key findings demonstrate that configurations such as Leaky ReLU Activation and Batch Norm Layer consistently improve accuracy, results highlight the flexibility and robustness of AMRAE. The results underscore the significance of AMRAE in addressing the limitations of traditional dimensionality reduction methods, with potential applications in various fields requiring high-dimensional data analysis. This paper provides a comprehensive summary of the objectives, methodology, results, and conclusions, showcasing AMRAE's efficacy in improving data representation and machine learning performance.
SN  - request pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{SadeghZadeh2026Adaptive,
  author = {Seyed-Ali Sadegh-Zadeh and Nasrin Sadeghzadeh and Saeed Shiry Ghidary and Sobhan Movahedi and Kaveh Kavianpour and Mohammad Amin Barati and Alireza Soleimani Mamalo},
  title = {Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) for Effective Dimensionality Reduction},
  journal = {Frontiers in Biomedical Signal Processing},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {79-104},
  doi = {10.62762/FBSP.2025.800185},
  url = {https://www.icck.org/article/abs/FBSP.2025.800185},
  abstract = {The Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) algorithm is introduced as a novel dimensionality reduction technique that integrates manifold learning with autoencoders to capture the intrinsic geometry of high-dimensional data effectively. The study evaluates the impact of various adjustments and enhancements within the "Manifold Adjustment Box," including different regularization techniques, activation functions, and architectural choices, across diverse datasets. Key findings demonstrate that configurations such as Leaky ReLU Activation and Batch Norm Layer consistently improve accuracy, results highlight the flexibility and robustness of AMRAE. The results underscore the significance of AMRAE in addressing the limitations of traditional dimensionality reduction methods, with potential applications in various fields requiring high-dimensional data analysis. This paper provides a comprehensive summary of the objectives, methodology, results, and conclusions, showcasing AMRAE's efficacy in improving data representation and machine learning performance.},
  keywords = {dimensionality reduction, manifold concept regularized autoencoders, machine learning, high-dimensional data},
  issn = {request pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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