Abstract
In multi-label learning, each training example is represented by an instance while associated with multiple class labels simultaneously. Most existing approaches make use of multi-label training examples by utilizing the logical labeling information, i.e., one class label is either fully relevant or irrelevant to the instance. In this paper, a novel multi-label learning approach is proposed which aims to enhance the labeling information by extending logical labels into numerical labels. Firstly, a stacked matrix is constructed where the feature and the logical label matrix are placed vertically. Secondly, the labeling information is enhanced by leveraging the underlying low-rank structure in the stacked matrix. Thirdly, the multi-label predictive model is induced by the learning procedure from training examples with numerical labels. Extensive comparative studies clearly validate the advantage of the proposed method against the state-of-the-art multi-label learning approaches.
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Notes
- 1.
The data sets can be downloaded from: http://meka.sourceforge.net/#datasets and http://mulan.sourceforge.net/datasets.html.
References
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-09823-4_34
Zhang, M.-L., Zhou, Z.-H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Rubin, T., Chambers, A., Smyth, P., Steyvers, M.: Statistical topic models for multi-label document classification. Mach. Learn. 88(1–2), 157–208 (2012)
Yang, B., Sun, J.-T., Wang, T., Chen, Z.: Effective multilabel active learning for text classification. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, pp. 917–926 (2009)
Cabral, R., Torre, F., Costeira, J., Bernardino, A.: Matrix completion for multi-label image classification. In: Proceedings of 24th International Conference on Neural Information Processing Systems, Granada, Spain, pp. 190–198 (2011)
Wang, H., Huang, H., Ding, C.: Image annotation using multi-label correlated green’s function. In: Proceedings of 12th IEEE International Conference on Computer Vision, Kyoto, Japan, pp. 2029–2034 (2009)
Lo, H.-Y., Wang, J.-C., Wang, H.-M., Lin, S.-D.: Costsensitive multi-label learning for audio tag annotation and retrieval. IEEE Trans. Multimedia 13(3), 518–529 (2011)
Sanden, C., Zhang, J.-Z.: Enhancing multi-label music genre classification through ensemble techniques. In: Proceedings of 34th ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, pp. 705–714 (2011)
Wang, J., Zhao, Y., Wu, X., Hua, X.-S.: A transductive multi-label learning approach for video concept detection. Pattern Recogn. 44(10–11), 2274–2286 (2011)
Hou, P., Geng, X., Zhang, M.-L.: Multi-label manifold learning. In: Proceedings of 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, pp. 1680–1686 (2016)
Liu, G., Lin, Z.-C., Yang, S.-C., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intel. 35(1), 171–184 (2013)
Eriksson, B., Balzano, L., Nowak, R.: High-rank matrix completion. In: Proceedings of 15th International Conference on Artificial Intelligence Statistics, La Palma, Canary Islands, vol. 20, pp. 373–381 (2012)
Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)
Zhang, M.-L., Zhou, Z.-H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)
Elisseeff, A., Weston, J.: A kernel method for multilabelled classification. In: Proceedings of Advance Neural Information Processing Systems 14, Vancouver, Canada, pp. 681–687 (2001)
Frnkranz, J., Hllermeier, E., Menca, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)
Tai, F., Lin, H.-T.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508–2542 (2012)
Sun, L., Ji, S., Ye, J.: Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Trans. Pattern Anal. Mach. Intel. 33(1), 194–200 (2011)
Li, Y.-K., Zhang, M.-L., Geng, X.: Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of 15th IEEE International Conference on Data Mining, Atlantic City, NJ, pp. 251–260 (2015)
Ma, S.-Q., Goldfarb, D., Chen, L.-F.: Fixed point and bregman iterative methods for matrix rank minimization. Math. Programm. 128(1–2), 321–353 (2011)
Pérez-Cruz, F., Vázquez, A., Alarcón-Diana, P., Artés-RodrÃguez, A.: An IRWLS procedure for SVR. In: 10th European Conference on Signal Processing, Tampere, Finland, pp. 1–4 (2000)
Schlkopf, B., Smola, A.: Learning with Kernels. The MIT Press, Berlin (2001)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random klabelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)
Acknowledgements
This research was supported by the National Key Research & Development Plan of China (No. 2017YFB1002801), the National Science Foundation of China (61622203), the Jiangsu Natural Science Funds for Distinguished Young Scholar (BK20140022), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Collaborative Innovation Center of Wireless Communications Technology.
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Tao, A., Xu, N., Geng, X. (2018). Labeling Information Enhancement for Multi-label Learning with Low-Rank Subspace. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_51
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