Abstract
Multi-label learning annotates a data point with the relevant labels. Under the low-rank assumption, many approaches embed the label space into the low-dimension space to capture the label correlation. However these approaches usually have weak prediction performance because the low-rank assumption is usually violated in real-world applications. In this paper, we observe the fact that the linear representation of row and column vectors of label matrix does not depend on the rank structure and it can capture the linear geometry structure of label matrix (LGSLM). Inspired by the fact, we propose the LGSLM classifier to improve the prediction performance. More specifically, after rearranging the columns of a label matrix in decreasing order according to the number of positive labels, we capture the linear representation of the row vectors of the compact region in the label matrix. Moreover, we also capture the linear and sparse representation of column vectors using the \(L_1\)-norm. The experimental results for five real-world datasets show the superior performance of our approach compared with state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: risk bounds and structural results. J. Mach. Learn. Res. 3(Nov), 463–482 (2002)
Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Proceedings of NeurIPS, pp. 730–738 (2015)
Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.A.: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47979-1_7
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: Proceedings of ICCV, pp. 2790–2797 (2002)
Gupta, V., Wadbude, R., Natarajan, N., Karnick, H., Jain, P, Rai, P.: Distributional semantics meets multi-label learning. In: Proceedings of AAAI, pp. 3747–3754 (2019)
Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2007)
Jain, H., Prabhu, Y., Varma, M.: Extreme multi-label loss functions for recommendation, tagging, ranking and other missing label applications. In: Proceedings of SIGKDD, pp. 935–944 (2016)
Jing, L., Yang, L.: Semi-supervised low-rank mapping learning for multi-label classification. In: Proceedings of CVPR, pp. 1483–1491 (2015)
Li, J., Lu, K., Huang, Z., Shen, H.Y.: Two birds one stone: on both cold-start and long-tail recommendation. In: Proceedings of ACM MM, pp. 898–906 (2017)
Liu, W., Tsang, I.W.: Large margin metric learning for multi-label prediction. In: Proceedings of AAAI (2015)
Shen, X., Liu, W., Tsang, I.W., Sun, Q.S., Ong, Y.S.: Compact multi-label learning. In: Proceedings of AAAI (2018)
Sorower, M.S.: A literature survey on algorithms for multi-label learning. Oregon State University, vol. 18, pp. 1–25 (2010)
Spyromitros, X.E., Papadopoulos, S., Kompatsiaris, I.Y., Tsoumakas, G., Vlahavas, I.: A comprehensive study over VLAD and product quantization in large-scale image retrieval. IEEE Trans. Multimedia 16(6), 1713–1728 (2014)
Tai, F., Lin, H.T.: Multi-label classification with principal label space transformation. Neural Comput. 24(9), 2508–2542 (2012)
Turnbull, D., Barrington, L., Torres, D., Lanckriet, G.: Semantic annotation and retrieval of music and sound effects. IEEE Trans. Audio Speech Lang. Process. 16(2), 467–476 (2008)
Xu, C., Tao, D., Xu, C.: Robust extreme multi-label learning. In: Proceedings of SIGKDD, pp. 1275–1284 (2016)
Yu, H.F., Jain, P., Kar, P., S. Dhillon, I.: Large-scale multi-label learning with missing labels. In: Proceedings of ICML, pp. 593–601 (2014)
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2013)
Zhang, Y., Schneider, J.: Multi-label output codes using canonical correlation analysis. In: Proceedings of AISTATS, pp. 873–882 (2011)
Zhang, Y., Schneider, J.: Maximum margin output coding. In: Proceedings of ICML (2012)
Mencia, E.L., Fürnkranz, J.: Efficient pairwise multilabel classification for large-scale problems in the legal domain. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 50–65 (2002)
Acknowledgement
This work is supported by the National Key Research and Development Program of China (No. 2016QY06X1203), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDC02040400), the National Natural Science Foundation of China (No. U1836203) and Shandong Provincial Key Research and Development Program (2019JZZY20127).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, T., Li, F., Zhuang, F., Guo, Y., Fang, L. (2020). The Linear Geometry Structure of Label Matrix for Multi-label Learning. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-59051-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59050-5
Online ISBN: 978-3-030-59051-2
eBook Packages: Computer ScienceComputer Science (R0)