High-Dimensional Statistics: Challenges and Solutions in Big Data Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 15
Special Issue Editors
Interests: high dimensional statistics; image and signal processing; wavelet transforms; gene expression; genomics
Interests: Bayesian modeling and computation; robust dimension reduction; manifold learning; ultrahigh-dimensional variable selection; Bayesian cluster process; algorithms for threat detection; medical image reconstruction; missingness imputation
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Special Issue Information
Dear Colleagues,
We are experiencing a wealth of information in the natural sciences, and the size of data from research and experiments continues to increase (both in dimensionality and in physical storage). Dimensionality reduction is crucial in terms of statistical data analysis to identify features without losing crucial information pertaining to inference. In addition, it is significant to reduce the size of available data, especially those dealing with big data, such as genomics, astronomy, medical imaging, and others. This presents new challenges in terms of compressing and modifying data without losing information potential to new findings while maintaining data integrity.
The primary focus of this special issue is on dimensionality reduction in terms of statistical inference and feature extraction, and foundations of computer science that would provide data handling, data management, and computational strategies that could offer versatility in dissemination of scientific findings. The special issue would serve as a potential resource for innovation and technological advancement utilizing the principles of computer and data science, and statistics.
Dr. Senthil Balaji Girimurugan
Dr. Hsin-Hsiung Huang
Guest Editors
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Keywords
- high dimensional statistics
- feature extraction
- medical imaging
- genomics
- neuroscience
- astronomy
- compression and error correction
- machine learning algorithms
- region of interest compression
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