This special issue will provide a modern look on recent trends and advances on statistical signal processing towards data science that account for a) complexity of the data which can be represented as low rank structures and subspaces, sparsity and missing values, or due to sheer variety of the data b) large scale settings which refers to high-dimensionality but also to the settings where sample size is smaller or not much larger than the dimension and hence make asymptotically optimal methods perform poorly and c) dynamic nature of the data which accumulates or streams at fast pace.
Prospective authors are invited to submit high-quality original contributions and reviews for this Special Issue. Potential topics include, but are not limited to:
- random matrix theory
- large-scale statistical inference and learning
- robust statistics
- large-scale optimization and optimization on manifolds
- regularization techniques and sparsity-driven approaches
- new representations and models to handle such data structures including graph signal processing, tensor data analysis and multi-linear algebra, latent-variable analysis models, and sparse signal representations and dictionaries.
Submission deadline: May 1st, 2019
Anticipated publication: December 1st, 2019
Guest Editors: Esa Ollila, Michael Muma and Frédéric Pascal