Advances in High-Dimensional Data Analysis and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 1556
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue compiles innovative statistical methodologies, applications, and data analyses that address the challenges and opportunities presented by high-dimensional data across various scientific disciplines. High-dimensional data, characterized by a large number of variables relative to the number of observations, are becoming increasingly prevalent in fields such as omics, electronic health records, imaging, and finance. This Special Issue will feature a broad range of articles, including the following. Novel statistical techniques: introduction of new statistical models and inference methods tailored to handle high-dimensional datasets, ensuring robustness and accuracy in the face of data complexity. Machine learning algorithms: development and application of advanced machine learning algorithms that can efficiently manage and analyze high-dimensional data, uncovering hidden patterns and driving predictive analytics. Dimensionality reduction methods: innovative approaches to reduce the dimensionality of data while preserving essential information, facilitating more efficient data analysis and visualization. Computational techniques: exploration of novel computational methods and tools designed to process high-dimensional data more effectively, addressing issues related to computational cost and scalability. Practical applications: case studies and practical applications demonstrating the successful implementation of high-dimensional data analysis techniques in various domains, such as personalized medicine, biomarker identification, financial modeling, and image recognition.
Dr. Ran Dai
Guest Editor
Manuscript Submission Information
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Keywords
- high-dimensional data
- statistical techniques
- machine learning algorithms
- dimensionality reduction methods
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