Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification
Abstract
Label-specific features serve as an effective strategy to facilitate multi-label classification, which account for the distinct discriminative properties of each class label via tailoring its own features. Existing approaches implement this strategy in a quite straightforward way, i.e. finding the most pertinent and discriminative features for each class label and directly inducing classifiers on constructed label-specific features. In this paper, we propose a dual perspective for label-specific feature learning, where label-specific discriminative properties are considered by identifying each label’s own non-informative features and making the discrimination process immutable to variations of these features. To instantiate it, we present a perturbation-based approach DELA to provide classifiers with label-specific immutability on simultaneously identified non-informative features, which is optimized towards a probabilistically-relaxed expected risk minimization problem. Comprehensive experiments on 10 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.
BibTeX
@InProceedings{pmlr-v162-hang22a,
title = {Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification},
author = {Hang, Jun-Yi and Zhang, Min-Ling},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {8375--8386},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/hang22a/hang22a.pdf},
url = {https://proceedings.mlr.press/v162/hang22a.html},
abstract = {Label-specific features serve as an effective strategy to facilitate multi-label classification, which account for the distinct discriminative properties of each class label via tailoring its own features. Existing approaches implement this strategy in a quite straightforward way, i.e. finding the most pertinent and discriminative features for each class label and directly inducing classifiers on constructed label-specific features. In this paper, we propose a dual perspective for label-specific feature learning, where label-specific discriminative properties are considered by identifying each label’s own non-informative features and making the discrimination process immutable to variations of these features. To instantiate it, we present a perturbation-based approach DELA to provide classifiers with label-specific immutability on simultaneously identified non-informative features, which is optimized towards a probabilistically-relaxed expected risk minimization problem. Comprehensive experiments on 10 benchmark data sets show that our approach outperforms the state-of-the-art counterparts.}
}