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Laplacian regularized low-rank sparse representation transfer learning

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Abstract

In unsupervised transfer learning, it is extremely valuable to effectively extract knowledge from the vast amount of untagged data that exists by utilizing tagged data from other similar databases. In general, the data in the real world often resides in the low-dimensional manifold embedded in the high-dimensional environment space. However, the current subspace transfer learning methods do not consider the nonlinear geometry structure inside the data, so the local similarity information between the data may be lost in the learning process. In order to improve this respect, we propose a new subspace transfer learning algorithm, namely Laplacian Regularized Low-Rank Sparse Representation Transfer Learning (LRLRSR-TL). After introducing the low-rank representation and sparse constraints, the method incorporates Laplacian regularization term to represent the global low-dimensional structure and capture the inherent nonlinear geometry information of the data. Experimental investigation conducted based on five different cross-domain visual image datasets shows that the proposed method has outstanding performance compared with several state-of-the-art transfer learning methods.

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Acknowledgements

This work is supported by the National Key R&D Program of China (Grant Nos. 2018YFC2001600, 2018YFC2001602), and the National Natural Science Foundation of China under Grant no. 61473150.

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  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Lin Guo & Qun Dai

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  1. Lin Guo
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Guo, L., Dai, Q. Laplacian regularized low-rank sparse representation transfer learning. Int. J. Mach. Learn. & Cyber. 12, 807–821 (2021). https://doi.org/10.1007/s13042-020-01203-6

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