Abstract
The redundant sources problem in multi-source learning always exists in various real-world applications such as multimedia analysis, information retrieval, and medical diagnosis, in which the heterogeneous representations from different sources always have three-way redundancies. More seriously, the redundancies will cost a lot of storage space, cause high computational time, and degrade the performance of learner. This paper is an attempt to jointly reduce redundant sources. Specifically, a novel Heterogeneous Manifold Smoothness Learning (HMSL) model is proposed to linearly map multi-source data to a low-dimensional feature-isomorphic space, in which the information-correlated representations are close along manifold while the semantic-complementary instances are close in Euclidean distance. Furthermore, to eliminate three-way redundancies, we present a new Correlation-based Multi-source Redundancy Reduction (CMRR) method with 2,1-norm equation and generalized elementary transformation constraints to reduce redundant sources in the learned feature-isomorphic space. Comprehensive empirical investigations are presented that confirm the promise of our proposed framework.
Keywords
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This work was supported in part by National Natural Science Foundation of China (No. 61601458).
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Zhang, L., Wang, S., Jin, X., Jia, S. (2020). Joint Multi-source Reduction. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_18
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