Label embedding with Partial Heterogeneous Contexts

Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix factorization formula to effectively exploit the label relatedness in each context. With a shared embedding principle across heterogeneous contexts, the label relatedness is selectively aligned in a shared space. Due to our elegant formulation, PHCLE overcomes the partial context problem and can nicely incorporate more contexts, which both cannot be tackled with existing multi-context label embedding methods. An effective alternative optimization algorithm is further derived to solve the sparse matrix factorization problem. Experimental results demonstrate that the label embeddings obtained with PHCLE achieve superb performance in image classification task and exhibit good interpretability in the downstream label similarity analysis and image understanding task.
Original languageEnglish
Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
EditorsPascal Van Hentenryck, Zhi-Hua Zhou
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages4926-4933
Number of pages8
ISBN (Electronic)9781577358091
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2019 - Honolulu, United States of America
Duration: 27 Jan 20191 Feb 2019
Conference number: 33rd
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence 2019
Abbreviated titleAAAI 2019
CountryUnited States of America
CityHonolulu
Period27/01/191/02/19
Internet address

Cite this

Shi, Y., Xu, D., Pan, Y., Tsang, I. W., & Pan, S. (2019). Label embedding with Partial Heterogeneous Contexts. In P. Van Hentenryck, & Z-H. Zhou (Eds.), Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence (pp. 4926-4933). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 33, No. 1). Association for the Advancement of Artificial Intelligence (AAAI). https://doi.org/10.1609/aaai.v33i01.33014926