论文标题
词典学习，具有均匀稀疏表示的异常检测
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
论文作者
论文摘要
音频和图像处理等许多应用程序表明，稀疏表示是一种强大而有效的信号建模技术。找到一个最佳词典，同时生成数据的最稀少表示和最小的近似误差，这是字典学习（DL）解决的困难问题。我们研究DL在检测信号数据集中的异常样品时的性能。在本文中，我们使用特定的DL公式，该公式寻求均匀的稀疏表示模型来检测数据集中大多数样品的基本子空间，并使用K-SVD型算法。数值模拟表明，可以有效地使用此结果子空间来区分常规数据点的异常。
Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data and the smallest approximation error is a hard problem approached by dictionary learning (DL). We study how DL performs in detecting abnormal samples in a dataset of signals. In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm. Numerical simulations show that one can efficiently use this resulted subspace to discriminate the anomalies over the regular data points.
