Neurocomputing-Special Issue on Machine Learning for Non-Gaussian Data Processing

Call for Papers

With the widespread explosion of sensing and computing, an increasing number of industrial applications and an ever-growing amount of academic research generate massive multi-modal data from multiple sources. Gaussian distribution is the probability distribution ubiquitously used in statistics, signal processing, and pattern recognition. However, not all the data we are processing are Gaussian distributed. It has been found in recent studies that explicitly utilizing the non-Gaussian characteristics of data (e.g., data with bounded support, data with semi-bounded support, and data with L₁/L₂-norm constraint) can significantly improve the performance of practical systems. Hence, it is of particular importance and interest to make thorough studies of the non-Gaussian data and the corresponding non-Gaussian statistical models (e.g., beta distribution for bounded support data, gamma distribution for semi-bounded support data, and Dirichlet/vMF distribution for data with L₁/L₂-norm constraint).

In order to analyze and understand such kind of non-Gaussian data, the developments of related learning theories, statistical models, and efficient algorithms become crucial. The scope of this special issue is to provide theoretical foundations and ground-breaking models and algorithms to solve this challenge.

We invite authors to submit articles to address the aspects ranging from case studies of particular problems with non-Gaussian distributed data to novel learning theories and approaches, including (but not limited to):

• Machine Learning for Non-Gaussian Statistical Models
• Non-Gaussian Pattern Learning and Feature Selection
• Sparsity-aware Learning for Non-Gaussian Data
• Visualization of Non-Gaussian Data
• Dimension Reduction and Feature Selection for Non-Gaussian Data
• Non-Gaussian Convex Optimization
• Non-Gaussian Cross Domain Analysis
• Non-Gaussian Statistical Model for Multimedia Signal Processing
• Non-Gaussian Statistical Model for Source and/or Channel Coding
• Non-Gaussian Statistical Model for Biomedical Signal Processing
• Non-Gaussian Statistical Model for Bioinformatics
• Non-Gaussian Statistical Model in Social Networks
• Platforms and Systems for Non-Gaussian Data Processing

Timeline