Heterogeneous transfer clustering for partial Co-occurrence data

Xiangyang Ye, Liu Yang, Qinghua Hu, Chenyang Shen, Liping Jing, Zhibin Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heterogeneous transfer clustering can translate knowledge from some related heterogeneous source domains to the target domain without any supervision. Existing works usually use a large amount of complete co-occurrence data to learn the projection functions mapping heterogeneous data to a common latent feature subspace. However, in many real applications, it is not practical to collect abundant co-occurrence data, while the available co-occurrence data are always incomplete. Another commonly encountered problem is that the complex structure of real heterogeneous data may result in substantial degeneration in clustering performance. To address these issues, we propose a heterogeneous transfer clustering method specifically designed for partial co-occurrence data (HTCPC). It is superior to the existing methods in three facets. First, HTCPC fully uses the partial co-occurrence data in both source and target domains to learn a latent space, maximally extracting useful knowledge for clustering from limited information. Second, it incorporates multi-layer hidden representations, accurately preserving the complex hierarchical structure of data. Third, it enforces approximately orthogonal constraint in representations, effectively characterizing the latent subspace with minimal redundancy. An efficient algorithm has been derived and implemented to realize the proposed HTCPC. A series of experiments on the real datasets have illustrated the advantage of the proposed approach compared with state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 31st International Conference on Tools with Artificial Intelligence, ICTAI 2019
PublisherIEEE Computer Society
Pages1042-1049
Number of pages8
ISBN (Electronic)9781728137988
DOIs
StatePublished - Nov 2019
Event31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019 - Portland, United States
Duration: Nov 4 2019Nov 6 2019

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2019-November
ISSN (Print)1082-3409

Conference

Conference31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019
CountryUnited States
CityPortland
Period11/4/1911/6/19

Keywords

  • Approximately-orthogonal-constraint
  • Heterogeneous-transfer-clustering
  • Hierarchical-structure
  • Partial-cooccurrence-data

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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