Cautious active clustering
Cautious active clustering
复制标题
谨慎主动集群
DOI：
10.1016/j.acha.2021.02.002
复制
发表时间：
2021
影响因子：
2.5
通讯作者：
Mhaskar, H.N.
中科院分区：
文献类型：
--
作者：
Cloninger, A.;Mhaskar, H.N.
我们考虑欧氏空间上从未知概率测度采样的点的分类问题。我们研究的问题，查询类标签在一个非常小的数量明智地选择的点，以便能够附加适当的类标签的每一个点在集合中。我们的方法是考虑未知的概率测度作为每个类的条件概率的凸组合。我们的技术涉及到使用一个高度本地化的内核从厄米多项式构造，以创建一个层次的估计支持的组成概率措施。我们不需要对任何概率测度的性质做任何假设，也不需要事先知道所涉及的类的数量。我们给出了理论上的保证，衡量我们的分类方案的F-分数。实例包括超光谱图像中的分类和MNIST分类。
We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. We study the question of querying the class label at a very small number of judiciously chosen points so as to be able to attach the appropriate class label to every point in the set. Our approach is to consider the unknown probability measure as a convex combination of the conditional probabilities for each class. Our technique involves the use of a highly localized kernel constructed from Hermite polynomials, in order to create a hierarchical estimate of the supports of the constituent probability measures. We do not need to make any assumptions on the nature of any of the probability measures nor know in advance the number of classes involved. We give theoretical guarantees measured by theF-score for our classification scheme. Examples include classification in hyper-spectral images and MNIST classification.