We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data, NECA can support important downstream data mining tasks, such as clustering. Extensive experimental analysis demonstrated the effectiveness of NECA.

翻译：我们建议采用NECA, 这是一种用于绝对数据的深层代表性学习方法。 NECA建立在网络嵌入和深层不受监督的代表性学习的基础之上,深深地嵌入了属性值之间的内在关系,并用数字矢量表示明确表达数据对象。NECA专门为绝对数据设计,可以支持重要的下游数据挖掘任务,如集群。广泛的实验分析证明了NECA的有效性。