论文标题
合奏集群中的异质转移学习
Heterogeneous Transfer Learning in Ensemble Clustering
论文作者
论文摘要
这项工作提出了一种使用转移学习方法的合奏聚类方法。我们考虑了一个聚类问题，其中除了考虑的数据外，还可以使用“类似”标记的数据。可以用不同的功能描述数据集。该方法基于构建元功能，这些元功能描述了数据的结构特征及其从源到目标域的传递。使用蒙特卡洛建模对该方法的实验研究证实了其效率。与其他类似方法相比，提出的一种能够在源和目标域的任意特征描述下工作。它的复杂性较小。
This work proposes an ensemble clustering method using transfer learning approach. We consider a clustering problem, in which in addition to data under consideration, "similar" labeled data are available. The datasets can be described with different features. The method is based on constructing meta-features which describe structural characteristics of data, and their transfer from source to target domain. An experimental study of the method using Monte Carlo modeling has confirmed its efficiency. In comparison with other similar methods, the proposed one is able to work under arbitrary feature descriptions of source and target domains; it has smaller complexity.
