论文标题
无监督的词典学习异常检测
Unsupervised Dictionary Learning for Anomaly Detection
论文作者
论文摘要
我们调查了使用词典学习来解决大多数异常检测应用的要求，例如缺乏监督，在线配方，较低的假阳性率。我们在反洗钱申请中介绍了最近半监督在线算法的新结果。我们还引入了一种新型的无监督方法，即使用学习算法的性能作为样品性质的指示。
We investigate the possibilities of employing dictionary learning to address the requirements of most anomaly detection applications, such as absence of supervision, online formulations, low false positive rates. We present new results of our recent semi-supervised online algorithm, TODDLeR, on a anti-money laundering application. We also introduce a novel unsupervised method of using the performance of the learning algorithm as indication of the nature of the samples.
