Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

Jun-Yi Hang, Min-Ling Zhang
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8375-8386, 2022.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification %A Jun-Yi Hang %A Min-Ling Zhang %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-hang22a %I PMLR %P 8375--8386 %U https://proceedings.mlr.press/v162/hang22a.html %V 162 %X 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.
APA
Hang, J. & Zhang, M.. (2022). Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:8375-8386 Available from https://proceedings.mlr.press/v162/hang22a.html.

Related Material