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Learning non-parametric kernel via matrix decomposition for logistic regression
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DOI：10.1016/j.patrec.2023.05.018.png)
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Abstract
En 中文
Kernel logistic regression is a widely used method in machine learning applications. However, the com-mon kernels are not flexible or representative enough due to their simplistic parametric formulation. Non-parametric learning on kernels can enlarge the representation capability of the kernel, but the low -rank property is required to suppress the model complexity. To improve the flexibility of kernels, we perform low-rank decomposition on an adjust matrix, which can explicitly control the model complex-ity without using complicated rank penalty terms. In this paper, we also extend our method to learn an indefinite kernel by a different decom position. Experimental results demonstrate that the proposed learning method for non-parametric kernels outperforms other representative algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Non -parametric kernel
Indefinite kernel
Matrix decomposition
Kernel logistic regression
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