论文标题
深层图像嵌入聚类方法在表格数据上的有效性
Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data
论文作者
论文摘要
文献中的深度学习方法通常是基于图像数据集的基准标准的，这可能不是适合非图像表数据的基准。在本文中，我们采用以数据为中心的视图，以对表格数据进行深层嵌入集群进行最早的研究之一。在七个表格数据集上测试了为图像数据集提出的八种聚类和最新的嵌入聚类方法。我们的结果表明，一种传统的聚类方法在八种方法中排名第二，并且优于大多数深层嵌入聚类基线。我们的观察与文献相吻合，即对表格数据的常规机器学习仍然是针对深度学习的强大方法。因此，最新的嵌入聚类方法应考虑以数据为中心的学习体系结构的定制，以成为表格数据的竞争基线。
Deep learning methods in the literature are commonly benchmarked on image data sets, which may not be suitable or effective baselines for non-image tabular data. In this paper, we take a data-centric view to perform one of the first studies on deep embedding clustering of tabular data. Eight clustering and state-of-the-art embedding clustering methods proposed for image data sets are tested on seven tabular data sets. Our results reveal that a traditional clustering method ranks second out of eight methods and is superior to most deep embedding clustering baselines. Our observation aligns with the literature that conventional machine learning of tabular data is still a robust approach against deep learning. Therefore, state-of-the-art embedding clustering methods should consider data-centric customization of learning architectures to become competitive baselines for tabular data.
