Multi-Label Learning with Regularization Enriched Label-Specific Features

Ze-Sen Chen, Min-Ling Zhang
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:411-424, 2019.

Abstract

Multi-label learning learns from examples each associated with multiple class labels simultaneously, and the goal is to induce a predictive model which can assign a set of relevant labels for the unseen instance. Label-specific features serve as an effective strategy towards inducing multi-label predictive model, where the relevancy of each class label is determined by employing tailored features encoding inherent and distinct characteristics of the class label its own. In this paper, a regularization based approach named {\textsc{Reel}} is proposed for label-specific features generation, which works by enriching label-specific feature representation for each class label via synergizing informative label-specific features from other class labels with sparse regularization. Specifically, full-order label correlations are considered by {\textsc{Reel}} while the number of classifiers induced for multi-label prediction is linear to the number of class labels. Extensive experiments on fifteen benchmark multi-label data sets clearly show the favorable performance of {\textsc{Reel}} against other state-of-the-art multi-label learning approaches with label-specific features.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-chen19a, title = {Multi-Label Learning with Regularization Enriched Label-Specific Features}, author = {Chen, Ze-Sen and Zhang, Min-Ling}, pages = {411--424}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/chen19a/chen19a.pdf}, url = {http://proceedings.mlr.press/v101/chen19a.html}, abstract = {Multi-label learning learns from examples each associated with multiple class labels simultaneously, and the goal is to induce a predictive model which can assign a set of relevant labels for the unseen instance. Label-specific features serve as an effective strategy towards inducing multi-label predictive model, where the relevancy of each class label is determined by employing tailored features encoding inherent and distinct characteristics of the class label its own. In this paper, a regularization based approach named {\textsc{Reel}} is proposed for label-specific features generation, which works by enriching label-specific feature representation for each class label via synergizing informative label-specific features from other class labels with sparse regularization. Specifically, full-order label correlations are considered by {\textsc{Reel}} while the number of classifiers induced for multi-label prediction is linear to the number of class labels. Extensive experiments on fifteen benchmark multi-label data sets clearly show the favorable performance of {\textsc{Reel}} against other state-of-the-art multi-label learning approaches with label-specific features.} }
Endnote
%0 Conference Paper %T Multi-Label Learning with Regularization Enriched Label-Specific Features %A Ze-Sen Chen %A Min-Ling Zhang %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-chen19a %I PMLR %J Proceedings of Machine Learning Research %P 411--424 %U http://proceedings.mlr.press %V 101 %W PMLR %X Multi-label learning learns from examples each associated with multiple class labels simultaneously, and the goal is to induce a predictive model which can assign a set of relevant labels for the unseen instance. Label-specific features serve as an effective strategy towards inducing multi-label predictive model, where the relevancy of each class label is determined by employing tailored features encoding inherent and distinct characteristics of the class label its own. In this paper, a regularization based approach named {\textsc{Reel}} is proposed for label-specific features generation, which works by enriching label-specific feature representation for each class label via synergizing informative label-specific features from other class labels with sparse regularization. Specifically, full-order label correlations are considered by {\textsc{Reel}} while the number of classifiers induced for multi-label prediction is linear to the number of class labels. Extensive experiments on fifteen benchmark multi-label data sets clearly show the favorable performance of {\textsc{Reel}} against other state-of-the-art multi-label learning approaches with label-specific features.
APA
Chen, Z. & Zhang, M.. (2019). Multi-Label Learning with Regularization Enriched Label-Specific Features. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:411-424

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