Selective Labeling via Error Bound Minimization

no code implementations • NeurIPS 2012 • Quanquan Gu, Tong Zhang, Jiawei Han, Chris H. Ding

In particular, we derive a deterministic generalization error bound for LapRLS trained on subsampled data, and propose to select a subset of data points to label by minimizing this upper bound.

Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach

no code implementations • NeurIPS 2012 • Dijun Luo, Heng Huang, Feiping Nie, Chris H. Ding

In many graph-based machine learning and data mining approaches, the quality of the graph is critical.

BIG-bench Machine Learning Clustering +1

Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

no code implementations • NeurIPS 2010 • Feiping Nie, Heng Huang, Xiao Cai, Chris H. Ding

The ℓ2, 1-norm based loss function is robust to outliers in data points and the ℓ2, 1-norm regularization selects features across all data points with joint sparsity.

feature selection regression