B1713
Title: Optimization methodologies for big data analysis
Authors: Carisa Kwok Wai Yu - The Hang Seng University of Hong Kong (Hong Kong) [presenting]
Siu Kai Choy - The Hang Seng University of Hong Kong (Hong Kong)
Wai Leong Ng - The Hang Seng University of Hong Kong (Hong Kong)
Chi Chung Siu - The Hang Seng University of Hong Kong (Hong Kong)
Abstract: The explosive growth of big data brings opportunities and has benefited the development and the revolution of various disciplines. However, the curse of missing data, gross error and dimensionality is an arduous challenge for big data analysis. The low-rank matrix optimization is an important technique to deal with the curse of missing data and gross error in big data analysis in various disciplines. Sparse optimization is a popular and practical technique to deal with dimensionality for big data problems and has been successfully implemented in various fields. We will discuss new optimization methodologies with continuation techniques to solve the problems in big data analysis.
