Emerging Trends in Intelligent Data Analysis for Sparse, Noisy, and High-Dimensional Data

Submission Deadline: 29 February 2024 (closed) View: 162

Guest Editors

Dr. Ximing Li, Jilin University,China
Dr. Wanfu Gao, Jilin University,China
Dr. Changchun Li, Jilin University,China
Dr. Bo Fu, Liaoning Normal University,China

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Summary

Intelligent data analysis has become a crucial research area due to the exponential growth of data in various domains. However, the analysis of sparse, noisy, and high-dimensional data presents unique challenges that demand innovative approaches and techniques. This special issue aims to explore emerging trends in intelligent data analysis specifically tailored to address these challenges. We invite researchers and practitioners to contribute original research papers, review articles, and case studies highlighting recent advancements, methodologies, and applications in this field.

 

The special issue seeks high-quality contributions related to the analysis of sparse, noisy, and high-dimensional data, with a focus on intelligent data analysis techniques. We welcome submissions that explore related topics within the scope of intelligent data analysis for sparse, noisy, and high-dimensional data. The topics of interest include, but are not limited to:

 

1. Sparse representation learning and feature selection for high-dimensional data.

2. Noise reduction and data preprocessing techniques for noisy data analysis

3. Dimensionality reduction and feature extraction methods for high-dimensional data

4. Ensemble learning and meta-learning approaches for sparse and noisy data analysis

5. Deep learning and neural network models for sparse and high-dimensional data

6. Sparse signal processing and compressive sensing for data analysis

7. Transfer learning and domain adaptation techniques for sparse and noisy data

8. Clustering and classification methods for sparse and high-dimensional data

9. Anomaly detection and outlier analysis in noisy and high-dimensional data

10. Visualization and interpretation of sparse, noisy, and high-dimensional data

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Keywords

sparse, noisy, high-dimensional data, representation learning, feature selection, intelligent data analysis, classification and clustering, weakly-supervised, semi-supervised learning, domain adaptation, anomaly detection, visualization and interpretation

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