Isolation-based Spherical Ensemble Representations for Anomaly Detection

• URL: http://arxiv.org/abs/2510.13311v1
• Date: Wed, 15 Oct 2025 09:00:05 GMT
• Title: Isolation-based Spherical Ensemble Representations for Anomaly Detection
• Authors: Yang Cao, Sikun Yang, Hao Tian, Kai He, Lianyong Qi, Ming Liu, Yujiu Yang,
• Abstract summary: Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring.<n>Existing unsupervised anomaly detection methods face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types.<n>We propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity.
• Score: 60.989157958972356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring. Despite extensive research, existing unsupervised anomaly detection methods still face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types. To address these problems, we propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity. ISER constructs ensemble representations where hypersphere radii encode density information: smaller radii indicate dense regions while larger radii correspond to sparse areas. We introduce a novel similarity-based scoring method that measures pattern consistency by comparing ensemble representations against a theoretical anomaly reference pattern. Additionally, we enhance the performance of Isolation Forest by using ISER and adapting the scoring function to address axis-parallel bias and local anomaly detection limitations. Comprehensive experiments on 22 real-world datasets demonstrate ISER's superior performance over 11 baseline methods.

Related papers

• Stochastic Voronoi Ensembles for Anomaly Detection [6.508284558109273]
  We propose SVEAD (Stochastic Voronoi Ensembles Anomaly Detector), which constructs ensemble random Voronoi diagrams and scores points by normalized cell-relative distances weighted by local scale.<n> Experiments on 45 datasets demonstrate that SVEAD outperforms 12 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-01-07T07:37:38Z)
• Finding Time Series Anomalies using Granular-ball Vector Data Description [29.555765407206408]
  Granular-ball One-Class Network (GBOC) is a novel approach based on a data-adaptive representation called Granular-ball Vector Data Description (GVDD)<n>GVDD partitions the latent space into compact, high-density regions represented by granular-balls.<n>During inference, anomaly scores are computed based on the distance to the nearest granular-ball.
  arXiv  Detail & Related papers  (2025-11-15T10:24:53Z)
• Text Anomaly Detection with Simplified Isolation Kernel [58.13924648777626]
  Two-step approaches combine pre-trained large language model embeddings and anomaly detectors.<n>High-dimensional dense embeddings extracted by large language models pose challenges due to substantial memory requirements and high computation time.<n>We introduce the Simplified Isolation Kernel (SIK), which maps high-dimensional dense embeddings to lower-dimensional sparse representations.
  arXiv  Detail & Related papers  (2025-10-15T06:35:54Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [54.85000884785013]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• Robust Anomaly Detection via Tensor Pseudoskeleton Decomposition [0.0]
  Anomaly detection plays a critical role in modern data-driven applications, from identifying fraudulent transactions to safeguarding network infrastructure to monitoring sensor systems for irregular patterns.<n>Traditional approaches, such as distance, density, or cluster-based methods, face significant challenges when applied to high dimensional tensor data, where complex interdependencies across dimensions amplify noise complexity and computational robustness.<n>This paper leverages Chidori pseudoskeleton decomposition within a tensor-robust principal component analysis framework to extract low Tucker rank structure while isolating sparse anomalies, ensuring robustness to anomaly detection.
  arXiv  Detail & Related papers  (2025-02-14T05:34:57Z)
• Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection [0.0]
  Time series anomaly detection (TSAD) plays a vital role in many industrial applications.<n>Contrastive learning has gained momentum in the time series domain for its prowess in extracting meaningful representations from unlabeled data.<n>In this study, we propose a novel approach, CNT, that incorporates a window-based contrastive learning strategy fortified with learnable transformations.
  arXiv  Detail & Related papers  (2023-04-16T21:36:19Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection [42.585075865267946]
  We propose the Projected Sliced Wasserstein (PSW) autoencoder-based anomaly detection method. In particular, the computation-friendly eigen-decomposition method is leveraged to find the principal component for slicing the high-dimensional data. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of the proposed method.
  arXiv  Detail & Related papers  (2021-12-20T09:21:02Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Anomaly Detection with Domain Adaptation [5.457279006229213]
  We propose the Invariant Representation Anomaly Detection (IRAD) to solve this problem. The extraction is achieved by an across-domain encoder trained together with source-specific encoders and generators by adversarial learning. We evaluate IRAD extensively on digits images datasets (MNIST, USPS and SVHN) and object recognition datasets (Office-Home)
  arXiv  Detail & Related papers  (2020-06-05T21:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.