New Method Focused on Nonnegative Laplacian Embedding Accepted to IJCAI 2019
Tue May 14, 2019
Haoxuan Yang and Kai Liu have recently had their paper, “Learning Strictly Orthogonal p-Order Nonnegative Laplacian Embedding via Smoothed Iterative Reweighted Method,” accepted into the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).
This work studies Laplacian Embedding by using the p-th order of the l2-norm distances that satisfies both orthogonal and nonnegative constraints. A novel smoothed iterative reweighted method is introduced to tackle this challenging optimization problem and rigorously analyze its convergence. Extensive empirical studies on both synthetic and real data sets demonstrate the effectiveness and potential of the proposed method.
This paper will be presented this August in Macao, China. IJCAI is a top conference in Artificial Intelligence.