Paper ID: 2205.12752

NECA: Network-Embedded Deep Representation Learning for Categorical Data

Xiaonan Gao, Sen Wu, Wenjun Zhou

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.

Submitted: May 25, 2022