论文标题
无监督的学习本地最大值
Unsupervised Learning of the Set of Local Maxima
论文作者
论文摘要
本文描述了一种新的无监督学习形式，其输入是一组未标记的点，在向量空间的未知子集中，假定为未知值函数v的局部最大值。学习了两个函数：（i）一个集合指标C，它是二进制分类器，（ii）一个比较器函数h给定两个样品，预测哪些样品具有较高的未知函数v。损耗项的值较高，以确保所有训练样本x是v的局部最大值，根据H和满足C（x）= 1。因此，C和H相互提供训练信号：X附近的A点x'满足C（x）= -1或被H认为的值低于x。我们提出了一种算法，展示了一个示例，其中使用本地最大值作为指标功能比采用常规分类更有效，并得出合适的概括结合。我们的实验表明，该方法能够在异常检测任务中胜过单级分类算法，并且还提供了以完全无监督的方式提取的额外信号。
This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1. Therefore, c and h provide training signals to each other: a point x' in the vicinity of x satisfies c(x)=-1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.
