Multi-instance dictionary learning via multivariate performance measure optimization

Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin

Multi-instance dictionary learning via multivariate performance measure optimization

Files

1-s2.0-S0031320316304435-main.pdf (610.1 KB)

Type

Article

Authors

Wang, Jim Jing-Yan
Tsang, Ivor Wai-Hung
Cui, Xuefeng
Lu, Zhiwu
Gao, Xin

KAUST Department

Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date

2016-12-29

Print Publication Date

2017-06

Date

2016-12-29

Abstract

The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

Citation

Wang JJ-Y, Tsang IW-H, Cui X, Lu Z, Gao X (2016) Multi-instance dictionary learning via multivariate performance measure optimization. Pattern Recognition. Available: http://dx.doi.org/10.1016/j.patcog.2016.12.023.

Acknowledgements

We are grateful to Prof. Zhi-Hua Zhou for the fruitful discussions. The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).