查询增强主动度量学习
Query-augmented Active Metric Learning
摘要：
在本文中，我们提出了一个主动度量学习方法的聚类与成对约束。该方法主动查询信息实例对的标签，同时通过合并未标记的实例对来估计底层度量，从而导致更准确和高效的聚类过程。特别是，我们通过生成更多的成对标签来增加查询的约束，以在学习度量时提供额外的信息来提高聚类性能。此外，我们增加了度量学习的鲁棒性，通过更新学习的度量顺序和惩罚不相关的功能自适应。此外，我们还提出了一种新的主动查询策略，该策略通过引入邻域结构来更准确地评估实例对的信息增益，从而在不增加额外标签成本的情况下提高了聚类效率。在理论上，我们提供了一个更严格的误差界的建议度量学习方法，利用增强查询相比，使用现有的限制。此外，我们还研究了改进使用主动查询策略，而不是随机选择。仿真和真实数据集的数值研究表明，所提出的方法是特别有利的，当显着的功能和不相关的功能之间的信噪比低。
英文摘要：
In this paper we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process. In particular, we augment the queried constraints by generating more pairwise labels to provide additional information in learning a metric to enhance clustering performance. Furthermore, we increase the robustness of metric learning by updating the learned metric sequentially and penalizing the irrelevant features adaptively. In addition, we propose a novel active query strategy that evaluates the information gain of instance pairs more accurately by incorporating the neighborhood structure, which improves clustering efficiency without extra labeling cost. In theory, we provide a tighter error bound of the proposed metric learning method utilizing augmented queries compared with methods using existing constraints only. Furthermore, we also investigate the improvement using the active query strategy instead of random selection. Numerical studies on simulation settings and real datasets indicate that the proposed method is especially advantageous when the signal-to-noise ratio between significant features and irrelevant features is low.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
