Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning

Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning

Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1937-1943. https://doi.org/10.24963/ijcai.2017/269

Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster values with different granularities. It further models the couplings in value clusters within the same granularity and with different granularities to embed feature values into a new numerical space with independent dimensions. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods.
Keywords:
Machine Learning: Data Mining
Machine Learning: Unsupervised Learning