Abstract
The explosion of the label space degrades the performance of the classic multi-class learning models. Label space dimension reduction (LSDR) is developed to reduce the dimension of the label space by learning a latent representation of both the feature space and label space. Almost all existing models adopt a two-step strategy, i.e., first learn the latent space, and then connect the feature space with the label space by the latent space. Additionally, the latent space lacks interpretability for LSDR. In this paper, motivated by cross-modal learning, we propose a novel one-step model, named Quadruplet Dictionary Learning (QDL), for multi-label classification with many labels. QDL models the latent space by the representation coefficients, which own preeminent recoverability, predictability and interpretability. By simultaneously learning two dictionary pairs, the feature space and label space are well bi-directly bridged and recovered by four dictionaries. Experiments on benchmark datasets show that QDL outperforms the state-of-the-art label space dimension reduction algorithms.
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Acknowledge
This work was supported by the National Natural Science Foundation of China under Grants 61502332, 61876127 and 61732011, Natural Science Foundation of Tianjin under Grant 17JCZDJC30800, and Key Scientific and Technological Support Projects of Tianjin Key R&D Program under Grant 18YFZCGX00390 and Grant 18YFZCGX00680.
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Zheng, J., Zhu, W., Zhu, P. (2020). Multi-label Quadruplet Dictionary Learning. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_10
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