Learning with Partially Absorbing Random Walks

no code implementations • NeurIPS 2012 • Xiao-Ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-Fu Chang

We prove that under proper absorption rates, a random walk starting from a set $\mathcal{S}$ of low conductance will be mostly absorbed in $\mathcal{S}$.

Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming

no code implementations • NeurIPS 2009 • Xiao-Ming Wu, Anthony M. So, Zhenguo Li, Shuo-Yen R. Li

In this paper, we show that a large class of kernel learning problems can be reformulated as semidefinite-quadratic-linear programs (SQLPs), which only contain a simple positive semidefinite constraint, a second-order cone constraint and a number of linear constraints.

Computational Efficiency Constrained Clustering +1