Sparse Representation for Machine Learning

摘要

Sparse representation is a parsimonious principle that a signal can be approximated by a sparse superposition of basis functions. The main topic of my thesis research is to apply this principle in the machine learning fields including classification, feature extraction, feature selection, and optimization.