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Selective Labeling via Error Bound Minimization
Quanquan Gu · Tong Zhang · Chris Ding · Jiawei Han

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

In many practical machine learning problems, the acquisition of labeled data is often expensive and/or time consuming. This motivates us to study a problem as follows: given a label budget, how to select data points to label such that the learning performance is optimized. We propose a selective labeling method by analyzing the generalization error of Laplacian regularized Least Squares (LapRLS). 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. Since the minimization is a combinational problem, we relax it into continuous domain and solve it by projected gradient descent. Experiments on benchmark datasets show that the proposed method outperforms the state-of-the-art methods.

Author Information

Quanquan Gu (UCLA)
Tong Zhang (The Hong Kong University of Science and Technology)
Chris Ding (University of Texas at Arlington)
Jiawei Han (University of Illinois at Urbana-Champaign)

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