About this Research Topic
Since the turn of the century, there has been a surge of interest in research on data science. Techniques related to data science have become the main driving force behind numerous areas of industry and many new research directions have been developed, with new scientific questions raised from the study of important practical problems. More mathematicians, statisticians, and computer scientists continue to join this exciting, multidisciplinary area of research. However, in spite of great success in practical applications of data science in the last decade, many fundamental problems still remain unclear and further study into these areas is required.
This Research Topic will cover mathematical topics crucial to the advancement of data science including, but not limited to:
• applications of data science
• functional spaces suitable for big data analysis
• mathematical foundation of machine learning
• non-smooth convex or non-convex sparse optimization for data analysis
• scalable algorithms for big data
• signal image processing
• sparse representation of big data sets
• statistical analysis for big data
In this Research Topic, we aim to gather a collection of at least 10 research articles in the subjects mentioned above, to showcase the latest advancements in these fields. Original Research is encouraged and Review articles are also welcome.
Keywords: sparse representation, reproducing kernels, machine learning, image processing, non-convex optimization
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.