Text Anomaly Detection with Simplified Isolation Kernel (2025)

Abstract: 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 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.

## preliminaries ## methodology for text anomaly detection, we follow a two-step approach: text documents are first transformed into dense vector embeddings that capture semantic relationships. these embeddings can be generated using pre-trained language models, which encode contextual information and linguistic patterns. the quality of embeddings significantly influences downstream anomaly detection performance. once documents are embedded in a high-dimensional space, traditional anomaly detection algorithms can be applied to identify outliers. however, these embeddings typically exist in high-dimensional spaces and applying anomaly detection algorithms to such high-dimensional data creates substantial computational challenges. we introduce simplified isolation kernel (sik) to address these challenges, the key steps are shown in the following subsections. ## conclusion in this paper, we introduced the simplified isolation kernel (sik) for text anomaly detection. sik effectively overcomes the computational and memory challenges posed by dense text embeddings from pre-trained llms by mapping high-dimensional dense embeddings to a low-dimensional sparse space that preserves boundary information. the key innovation of sik lies in its boundary-focused feature mapping, which maintains a linear time complexity and significantly reduces the dimensionality of the feature representation.