Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
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DOI：
10.1109/icdmw.2019.00117
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发表时间：
2019-09
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影响因子：
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通讯作者：
Naoya Takeishi
中科院分区：
文献类型：
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作者：
Naoya Takeishi
提出了一种计算主成分分析（PCA）重构误差Shapley值的方法，该方法特别适用于解释基于PCA的异常检测结果。因为当应用基于PCA的异常检测时特征通常是相关的，所以在计算Shapley值的值函数时必须小心。我们利用PCA的概率观点，特别是它的条件分布，精确地计算Shapely值的值函数。我们还提出了数值的例子，这意味着Shapley值是有利的解释检测到的异常比每个功能的原始重建误差。
We present a method to compute the Shapley values of reconstruction errors of principal component analysis (PCA), which is particularly useful in explaining the results of anomaly detection based on PCA. Because features are usually correlated when PCA-based anomaly detection is applied, care must be taken in computing a value function for the Shapley values. We utilize the probabilistic view of PCA, particularly its conditional distribution, to exactly compute a value function for the Shapely values. We also present numerical examples, which imply that the Shapley values are advantageous for explaining detected anomalies than raw reconstruction errors of each feature.