论文标题
NECA：网络限制的深度表示学习
NECA: Network-Embedded Deep Representation Learning for Categorical Data
论文作者
论文摘要
我们提出了NECA，这是一种用于分类数据的深度表示学习方法。 NECA建立在网络嵌入的基础和深度无监督的表示学习的基础上，将属性值之间的固有关系深深嵌入，并明确表达了具有数字向量表示的数据对象。 NECA专为分类数据而设计，可以支持重要的下游数据挖掘任务，例如聚类。广泛的实验分析证明了NECA的有效性。
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.
