Supervised dimensionality reduction that preserves both global and local information

Dimensionality reduction is a problem of fundamental importance in both machine learning and data mining. In this paper, we develop a new approach that can process labeled datasets and accurately reduce their dimensionalities. The approach is based on a new objective that contains information from both the global and local structures of a data set. An… CONTINUE READING