Research Topic

Advances in Low-Rank Modeling and Sparse Modeling in Data Science

About this Research Topic

In recent years, low-rank and sparse modeling techniques have gained great success in multiple fields related to data science, including machine learning, computer vision and statistics, etc. Many mathematicians, statisticians, and computer scientists have contributed to this exciting, multidisciplinary area of research. However, some issues and fundamental problems still remain unclear and unsolved and further study into these areas is required.

This Research Topic will cover mathematical topics related to low-rank modeling and sparse modeling crucial to the advancement of data science including, but not limited to:

• Robust low-rank/sparse modeling for data analysis
• Robust principal component analysis (RPCA), Online algorithms for RPCA, etc.
• Relations between sparse/low-rank modeling and deep learning
• Robust sparse/low-rank subspace discovery
• Tensor data versions of the above problems
• Fast solvers for the non-smooth and nonconvex models
• Applications to robust image/video processing (e.g., denoising and restoration) and recognition, etc.


Keywords: Low-rank modeling, sparse modeling, machine learning, computer vision, image/video processing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In recent years, low-rank and sparse modeling techniques have gained great success in multiple fields related to data science, including machine learning, computer vision and statistics, etc. Many mathematicians, statisticians, and computer scientists have contributed to this exciting, multidisciplinary area of research. However, some issues and fundamental problems still remain unclear and unsolved and further study into these areas is required.

This Research Topic will cover mathematical topics related to low-rank modeling and sparse modeling crucial to the advancement of data science including, but not limited to:

• Robust low-rank/sparse modeling for data analysis
• Robust principal component analysis (RPCA), Online algorithms for RPCA, etc.
• Relations between sparse/low-rank modeling and deep learning
• Robust sparse/low-rank subspace discovery
• Tensor data versions of the above problems
• Fast solvers for the non-smooth and nonconvex models
• Applications to robust image/video processing (e.g., denoising and restoration) and recognition, etc.


Keywords: Low-rank modeling, sparse modeling, machine learning, computer vision, image/video processing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

14 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

14 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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