A1295
Title: Joint sparse optimization: Methodologies and its applications
Authors: Carisa Kwok Wai Yu - The Hang Seng University of Hong Kong (Hong Kong) [presenting]
Abstract: In many practical scenarios, gathering multiple measurement signals can greatly improve the quality of data analysis. The correlation between these related signals enables the simultaneous analysis of different variables, allowing their influences to be focused on common locations. Joint sparse optimization (JSO) takes advantage of this principle by capitalizing on the collective effect observed across various measurement signals, thereby enhancing model analysis and sparse recovery capability. The purpose is to explore popular numerical techniques used to tackle JSO challenges, investigating both their theoretical underpinnings and real-world applications. Additionally, a range of numerical tests is performed to assess and compare the efficiency and performance of these techniques, offering valuable insights into their effectiveness across different application areas. Through this investigation, the aim is to deepen the understanding of JSO and its significance in improving data analysis in several disciplines.
