About this Research Topic
Data analytics is a topic of paramount interest for a vast range of applications in the industry and the medical world. The last decade has seen a fast growing activity in the study of computational methods for large and/or heterogeneous data sets. Exciting problems at the core of the field are still waiting for fast and accurate algorithms.
The purpose of this Research Topic is to provide a forum for new research addressing the challenges of data analytics, unsupervised machine learning and statistical computation. The main focus will be the
• design and analysis of new methods for clustering and more general latent variable models,
• graphical models and Stochastics Block type of Models using spectral methods,
• convex optimisation e.g. using sparsity enforcing priors or nonconvex heuristics with computational guarantees for learning these models,
• high dimensional time series analysis, modelling and decomposition and their application to climate change, sensor networks, medical monitoring, etc,
• analysis of textual data and social networks, etc. We will also welcome new and exciting contributions to model selection theories and implementations for these various data types.
The Topic Editors welcome new and high quality contributions in the above mentioned fields of computational statistics and their application to application to medical imaging, genetics and biology, personalized medicine, the future agriculture, etc.
Keywords: Clustering, optimization, times series, high dimensional statistics
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.