Abstract
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 valueto-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.
Original language | English |
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Title of host publication | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence |
Editors | Carles Sierra |
Place of Publication | California |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 1937-1943 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Conference
Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |