Selective Labeling via Error Bound Minimization
Selective Labeling via Error Bound Minimization
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发表时间：
2012-12
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通讯作者：
Quanquan Gu;Tong Zhang;C. Ding;Jiawei Han
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作者：
Quanquan Gu;Tong Zhang;C. Ding;Jiawei Han
在许多实际的机器学习问题中，标记数据的获取通常是昂贵和/或耗时的。这促使我们研究如下问题：给定标签预算，如何选择要标记的数据点，以优化学习性能。通过分析Laplacian正则化最小二乘（LapRLS）的样本外误差，提出了一种选择性标记方法。特别是，我们推导出一个确定性的样本外误差界的LapRLS训练的二次采样数据，并建议选择一个子集的数据点标签通过最小化这个上限。由于最小化是一个组合问题，我们将其松弛到连续域，并解决它的投影梯度下降。在基准数据集上的实验表明，该方法的性能优于现有的方法。
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 out-of-sample error of Laplacian regularized Least Squares (LapRLS). In particular, we derive a deterministic out-of-sample 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.