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Random multi-scale kernel-based Bayesian distribution regression learning
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DOI：10.1016/j.knosys.2020.106073.png)
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The effective embedding estimation of distribution and the construction of regression model with strong representation ability are two key problems of distribution regression. This paper proposes a random multi-scale kernel-based Bayesian distribution regression (RMK-BDR) learning framework. Vector-valued kernel mean embedding (KME) estimators with a same dimension which is chosen adaptively to the data are introduced in the first stage of distribution regression learning. Then, a linear combination of multi-scale Gaussian kernels with different scale parameters randomly sampled from a predefined distribution is used as the regression model. Sparsity priors are added on those linear combination weights. Under the Bayesian inference theory, a prediction distribution of the response variable is obtained. A series of experiment results verify the effectiveness of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Distribution regression
Kernel mean embedding
Multi-scale kernel
Bayesian inference
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被引数:
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