\mathrm{MMD}[p,q]=\mathop{\mathrm{sup}}_{\|f\|_{\mathcal{H}}\leqslant1}(\mathrm{E}_pf(x)-\mathrm{E}_qf(y))\\ 在满足特定条件下, \mathrm{E}_pf(x) 为关于 f 的连续线性泛函,由Rieze表示定理知存在 \mu_p\in\mathcal{H} 使得\mathrm{E}_pf(x)= \left<\mu_p,f\right>_...
Domain adaptionRKHSMaximum mean difference (MMD)Lagrange multiplier method (LMM) optimizationSubspace learning of Reproducing Kernel Hilbert Space (RKHS) is most popular among domain adaption applications. The key goal is to embed the source and target domain samples into a common RKHS subspace where ...
算法方面RKHS和PCA、spline都有一些联系,比较应用了。Ref[3]最后和MMD的关系也很有意思,值得过一段时间认真研究下(see also "Maximum Mean Discrepancy Gradient Flow")【话说MMD是non-parametric inference里比较常用的distance,在选test fn是Lipschitz时和W1-dist有关,Paul Dupuis有用它和KL mix起来设计新的distance...
mmdregularizationsamplingrkhsgradient-flowsampling-methodsf-divergencewasserstein-gradient-flowsparticle-flowreproducing-kernel-hilbert-space UpdatedDec 20, 2024 Python Additive interaction modelling using I-priors kernelregressionrkhshilbert-spacesfisher-informationrkksempirical-bayeskrein-spacesreproducing-kernel ...
在支持向量机SVM中,通常使用核函数将样本输入空间转化为再生核Hilbert空间(Reproducing kernel Hilbert space,RKHS),提高算法处理非线性分类问题的性能。相比于Hilbert空间,RKHS有着很多优秀的性质。下面从RKHS的定义、RKHS刻画、RKHS与Hilbert空间关系等三个部分展开工作。
This test thrives on the same principle as the MMD (Max... J Kellner,A Celisse 被引量: 0发表: 0年 From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space. Assuming normality in the RKHS, we derive analytic expressions for probabilistic ...
A reasonable method should be that the MMD of the source and target domain data with the same label should be as small as possible after RKHS subspace transformation. However, the labels of target domain data are unknown and there is no way to model according to this criterion. In this ...
And the experiments show the superiority of MSE criterion, which performs better than the common Maximum Mean Difference (MMD) and the Covariance Matrix (CovM) criteria. Furthermore, considering the robustness of the RKHS subspace learning framework to the data dimension, we propose the domain ...