Mathematical Methods for Machine Learning and Computer Vision
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 2025 | Viewed by 1301
Special Issue Editor
Special Issue Information
Dear Colleagues,
Recent decades have witnessed the rapid developments of machine learning and computer vision, as well as their huge influence in many fields such as image recognition and classification, automatic driving, industrial manufacturing, medical imaging, to name a few. One of the crucial challenge in machine learning and computer vision is the development and utilization of different kinds of models and algorithms building bridges between data and the potential tasks. Over the past two decades, a number of mathematical methods represented by compressed sensing, high-order tensor modeling, and alternating direction method of multipliers, have been well developed to help construct the desired models and algorithms in machine learning and computer vision.
This Special Issue mainly focuses on the significant developments on the machine learning and computer vision, especially on their mathematical methods on related models and algorithms, including but not limited to those in the following topics: image recognition and classification, sparse/low-rank subspace clustering, convex clustering, compressed sensing, recommendation system, matrix/tensor completion, robust principal components analysis, alternating direction method of multipliers.
Prof. Dr. Jianjun Wang
Guest Editor
Manuscript Submission Information
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Keywords
- image recognition and classification
- subspace clustering
- convex clustering
- compressed sensing
- recommendation system
- matrix/tensor completion
- robust principal components analysis
- tensor modeling
- alternating direction method of multipliers
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