学习优化期望归一化割集的广义聚类
Generalized Clustering by Learning to Optimize Expected Normalized Cuts
摘要：
我们介绍了一种新的端到端的方法来学习集群在没有标记的例子。我们的聚类目标是基于优化规范化的削减，一个标准，衡量集群内的相似性以及集群间的相异性。我们定义了一个可微的损失函数等价于期望的归一化切割。与无监督深度学习中的大部分工作不同，我们的训练模型直接输出最终的聚类分配，而不是需要进一步处理才能使用的嵌入。我们的方法可以推广到各种领域的未知数据集，包括文本和图像。具体来说，我们在流行的无监督聚类基准上实现了最先进的结果（例如，MNIST、路透社、CIFAR-10和CIFAR-100），表现优于最强基线高达10.9%。我们的泛化结果优于（高达21.9%）最近的最高性能的聚类方法，具有泛化能力。
英文摘要：
We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. Our clustering objective is based on optimizing normalized cuts, a criterion which measures both intra-cluster similarity as well as inter-cluster dissimilarity. We define a differentiable loss function equivalent to the expected normalized cuts. Unlike much of the work in unsupervised deep learning, our trained model directly outputs final cluster assignments, rather than embeddings that need further processing to be usable. Our approach generalizes to unseen datasets across a wide variety of domains, including text, and image. Specifically, we achieve state-of-the-art results on popular unsupervised clustering benchmarks (e.g., MNIST, Reuters, CIFAR-10, and CIFAR-100), outperforming the strongest baselines by up to 10.9%. Our generalization results are superior (by up to 21.9%) to the recent top-performing clustering approach with the ability to generalize.
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。
