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• W3022924863 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." @default.
• W3022924863 created "2020-05-13" @default.
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• W3022924863 date "2020-01-01" @default.
• W3022924863 modified "2023-09-27" @default.
• W3022924863 title "Joint Multi-source Reduction" @default.
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• W3022924863 doi "https://doi.org/10.1007/978-3-030-46150-8_18" @default.
• W3022924863 hasPublicationYear "2020" @default.
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