Active sampling for graph-aware classification
Active sampling for graph-aware classification
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DOI：
10.1109/globalsip.2017.8309039
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Dimitris Berberidis;G. Giannakis
中科院分区：
文献类型：
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作者：
Dimitris Berberidis;G. Giannakis
目前的工作涉及数据自适应主动采样的图节点表示训练数据的二进制分类。可以使用节点特征之间的相似性度量来给出或构造图。利用图进行分类的前提是相邻节点上的标签根据分类马尔可夫随机场（MRF）进行相关。该模型被进一步放宽为具有连续值的标签的高斯（G）MRF，这种近似不仅减轻了分类模型的组合复杂性，而且还提供了未标记节点的最佳无偏软预测器。建议的采样策略是基于查询的节点，其标签的披露，预计造成最大的预期均方差的GMRF，一种策略，其中包括现有的方差最小化为基础的采样方法。一个简单而有效的启发式也被引入，以增加探索能力，并减少偏差的结果估计，考虑到模型标签预测的信心。新的采样策略是基于现成的数量，而不需要模型重新训练，使其可扩展到大型图形。使用合成和真实的数据的数值试验表明，所提出的方法达到的精度是相当或上级的最先进的，即使在减少运行时间。
The present work deals with data-adaptive active sampling of graph nodes representing training data for binary classification. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that labels over neighboring nodes are correlated according to a categorical Markov random field (MRF). This model is further relaxed to a Gaussian (G)MRF with labels taking continuous values, an approximation that not only mitigates the combinatorial complexity of the categorical model, but also offers optimal unbiased soft predictors of the unlabeled nodes. The proposed sampling strategy is based on querying the node whose label disclosure is expected to inflict the largest expected mean-square deviation on the GMRF, a strategy which subsumes the existing variance-minimization-based sampling method. A simple yet effective heuristic is also introduced for increasing the exploration capabilities, and reducing bias of the resultant estimator, by taking into account the confidence on the model label predictions. The novel sampling strategy is based on quantities that are readily available without the need for model retraining, rendering it scalable to large graphs. Numerical tests using synthetic and real data demonstrate that the proposed methods achieve accuracy that is comparable or superior to the state-of-the-art even at reduced runtime.