Title

Multi-Label Learning Via Codewords

Keywords

Hash Function Learning; Multi-label Learning; Structured Output Prediction; Structured Support Vector Machine

Abstract

In this paper, we introduce a novel hash learning framework for multi-label learning which employs structured prediction. A hash function is learned to embed samples in Hamming spaces, and for each label, a pair of codewords are simultaneously inferred from the available data. These codewords are then used to determine label predictions based on Hamming proximity. The key advantage of this framework is it's computational efficiency in tackling multi-label problems without making restrictive, simplifying assumptions about the structure of the output space, or developing problem-dependent heuristics. Our method not only enjoys considerably better scalability while capturing label inter-dependence, but also yields an exact training algorithm. Experimental results on a collection of benchmark multi-label datasets demonstrate that our model attains higher performance over alternative state-of-the-art multi-label approaches. It is also worth noting that our method can be extended to semi-supervised and missing labels scenarios.

Publication Date

12-13-2018

Publication Title

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volume

2018-November

Number of Pages

221-228

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICTAI.2018.00042

Socpus ID

85060790424 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85060790424

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