Data-Adaptive Active Sampling for Efficient Graph-Cognizant Classification
Data-Adaptive Active Sampling for Efficient Graph-Cognizant Classification
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DOI：
10.1109/tsp.2018.2866812
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发表时间：
2017-05
影响因子：
5.4
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
本文讨论了二进制分类的图节点表示训练数据的主动采样。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之间的标签根据分类马尔可夫随机场（MRF）进行相关。该模型进一步放宽到高斯（G）MRF与标签采取连续值的近似，不仅减轻了分类模型的组合复杂性，但也提供了最佳的无偏软预测的未标记的节点。建议的采样策略是基于查询的节点，其标签披露预计将造成最大的变化的GMRF，在这个意义上说，它是最翔实的平均。连接建立到其他采样方法，包括不确定性采样，方差最小化，和采样的基础上的$\Sigma \text{-}$最优性标准。一个简单而有效的启发式也被引入，以增加采样器的探索能力，并通过调整模型标签预测的置信度来减少所得分类器的偏差。新的采样策略是基于数量，是现成的，而不需要模型重新训练，使它们的计算效率和可扩展到大型图形。使用合成和真实的数据的数值试验表明，所提出的方法实现的精度是可比的或上级的最先进的，即使在减少运行时间。
This paper deals with 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 across 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 change on the GMRF, and in this sense it is the most informative on average. Connections are established to other sampling methods including uncertainty sampling, variance minimization, and sampling based on the $\Sigma \text{-}$ optimality criterion. A simple yet effective heuristic is also introduced for increasing the exploration capabilities of the sampler, and reducing bias of the resultant classifier, by adjusting the confidence on the model label predictions. The novel sampling strategies are based on quantities that are readily available without the need for model retraining, rendering them computationally efficient and 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.