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- W2970511004 abstract "In this paper, efficient algorithms are proposed to perform sparse coding for the features lifted to a high-dimensional space via nonlinear mapping. we developed how the well-known sparse coding algorithm Homotopy Iterative Thresholding(HIHT) algorithm can be made nonlinear with the kernel method. We also put forward the corresponding dictionary learning strategy using the Lagrange dual method. The experimental results we tested prove that the application of the kernel sparse coding in the classification problem is significantly improved compared with their linear counterparts." @default.
- W2970511004 created "2019-09-05" @default.
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- W2970511004 date "2018-08-01" @default.
- W2970511004 modified "2023-09-25" @default.
- W2970511004 title "Kernel-based Nonlinear Observation Method For Sparse Coding" @default.
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- W2970511004 doi "https://doi.org/10.1109/icinfa.2018.8812368" @default.
- W2970511004 hasPublicationYear "2018" @default.
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