自适应调整原子尺寸，提升异常检测准确率
Atom dimension adaptation for infinite set dictionary learning
- 根据信号贡献动态调整高斯与锥形原子的大小
- 降低表示误差，对依赖型异常检测效果更优
- 适合需要精准异常识别的场景
基于集合原子的字典学习在异常检测中展现出优势。不同于将原子视为单一向量，该方法允许从中心向量周围的集合中选取原子构建稀疏表示，集合可为锥形或带有概率分布。本文提出一种自适应调整高斯与锥形字典学习中集合原子尺寸的方法，旨在使原子大小与其在信号表示中的贡献相匹配。该算法不仅降低表示误差，还显著提升对‘依赖’类异常的检测性能，优于现有最先进方法。
原文摘要 · Abstract (English)
Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector; the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called `dependency'. We obtain better detection performance than state-of-the-art methods.
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