用轻量映射提升文本异常检测效率与精度
Text Anomaly Detection with Simplified Isolation Kernel
- 将高维语义嵌入映射为低维稀疏表示，聚焦边界特征
- 在7个数据集上超越11种顶尖算法，速度更快内存更少
- 适合需要高效部署的工业级异常检测场景
结合预训练大语言模型嵌入与异常检测器的两阶段方法，在文本异常检测中表现优异，得益于丰富的语义表征。然而，大语言模型提取的高维稠密嵌入带来巨大内存开销和计算延迟。为此，我们提出简化隔离核（Simplified Isolation Kernel, SIK），将高维稠密嵌入映射为低维稀疏表示，同时保留关键异常特征。SIK具有线性时间复杂度，通过创新的边界聚焦特征映射显著降低空间复杂度。在7个数据集上的实验表明，SIK在检测性能上优于11种现有最先进（SOTA）算法，同时保持计算高效和低内存消耗。所有代码与演示已开源至 https://github.com/charles-cao/SIK。
原文摘要 · Abstract (English)
Two-step approaches combining pre-trained large language model embeddings and anomaly detectors demonstrate strong performance in text anomaly detection by leveraging rich semantic representations. However, high-dimensional dense embeddings extracted by large language models pose challenges due to substantial memory requirements and high computation time. To address this challenge, we introduce the Simplified Isolation Kernel (SIK), which maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. SIK has linear time complexity and significantly reduces space complexity through its innovative boundary-focused feature mapping. Experiments across 7 datasets demonstrate that SIK achieves better detection performance than 11 state-of-the-art (SOTA) anomaly detection algorithms while maintaining computational efficiency and low memory cost. All code and demonstrations are available at https://github.com/charles-cao/SIK.
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