Online joint classification and anomaly detection via sparse coding
Online joint classification and anomaly detection via sparse coding
复制标题
通过稀疏编码进行在线联合分类和异常检测
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
J. Nelson
中科院分区：
文献类型：
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作者：
Freddie Kalaitzis;J. Nelson
我们提出了一种新的凸方案，用于在多变量时间序列设置下同时在线故障分类和异常检测。我们的方法扩展了最近在稀疏编码和异常检测方面的工作，使用过完备字典来解决已经存在一些异常分类的问题。数据的时间方面通过简单的滑动窗口方法处理；受群- lasso惩罚方法的启发，分类是通过l2对字典原子的系数群（稀疏编码）进行联合稀疏化处理；1 regularisation。驱动预测和编码的字典是给定的，并且可以通过一系列可用的先验算法来学习。我们在三相低压时间序列的分类和异常检测任务上展示了我们的框架。在这种情况下，我们基于影响低压电力线的常见故障的基本知识手动设计字典。由于这个原因，我们的方法并不一定需要一个训练阶段。
We present a novel convex scheme for simultaneous online fault classification and anomaly detection in a multivariate time-series setting. Our approach extends recent work on sparse coding and anomaly detection using an over-complete dictionary to problems where some taxonomy of anomalies already exists. The temporal aspect of the data is addressed by a simple sliding window approach; inspired by a group-LASSO penalisation approach, classification is treated by jointly sparsifying groups of the coefficients (the sparse coding) of dictionary atoms via ℓ2;1 regularisation. The dictionary which drives the prediction and coding is assumed given and is learnable by a range of available prior algorithms. We demonstrate our framework on a classification and anomaly detection task on three-phase low-voltage time-series. In this case, we manually design our dictionary based on basic knowledge of common faults that affect low-voltage powerlines. For this reason our approach does not necessarily require a training stage.