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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 #None

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 (Tencent)
Chris Ding (University of Texas at Arlington)
Jiawei Han (University of Illinois at Urbana-Champaign)

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