we propose the active label distribution learning via kernel maximum mean discrepancy(ALDL-kMMD) method to tackle this crucial but rarely studied problem.ALDL-kMMD captures the structural information of both data and label,extracts the most representative instances from the unlabeled ones by ...
In recent times, many real world applications have emerged that require estimates of class ratios in an unlabeled instance collection as opposed to labels of individual instances in the collection. In this paper we investigate the use of maximum mean discrepancy (MMD) in a repro...
Introduction Chapter 2. Maximum mean discrepancy, and a linear time estimate 2.1 ) MMD for a family of kernels 2.2 ) Empirical estimate of the MMD, asymptotic distribution, and test Chapter 3. Choice of kernel Chapter 4. Optimization procedure Chapter 5. Experiments Chapter 6. Conclusions...
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的文章,更general的dist和flow在“KALE Flow: A Re...
示例1: maximum_mean_discrepancy ▲点赞 5▼ # 需要导入模块: import utils [as 别名]# 或者: from utils importgaussian_kernel_matrix[as 别名]defmaximum_mean_discrepancy(x, y, kernel=utils.gaussian_kernel_matrix):r"""Computes the Maximum Mean Discrepancy (MMD) of two samples: x and y. ...
这种散度统计量D称为最大*均差(maximum mean discrepancy,MMD)[42]。当MMD很大时,这表明两个点过程是不同的。在经典的假设检验框架中,我们需要假设两个集合来源于同一个潜在随机过程的零假设下的MMD分布。我们可以从零分布中产生MMD值,方法是将两个条件中的样本混合并从混合中重新采样[24, 42]。下面的简单...
To this end, a Kernel Adaptive Filter (KAF) algorithm extracts the dynamic of each channel, relying on the similarity between multiple realizations through the Maximum Mean Discrepancy (MMD) criterion. To assemble dynamics extracted from all MoCap data, center kernel alignment (CKA) is used to ...
Distribution-free tests are constructed using maximum mean discrepancy (MMD) as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space (RKHS). For both scenarios, it is shown that as the number n of sequences goes to infinity, if the value of...
Given samples from distributions p and q, a two-sample test determines whether to reject the null hypothesis that p = q, based on the value of a test statistic measuring the distance between the samples. One choice of test statistic is the maximum mean discrepancy (MMD), which is a distan...
In order to further estimate the similarity between different groups, Maximum Mean Discrepancy (MMD) and hypothesis testing were applied. The application of KDE allows to visualize the distribution shape of each assemblage, while MMD measure the ‘mean distance’ between two assemblages when they are...