Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection

• URL: http://arxiv.org/abs/2411.06406v2
• Date: Wed, 20 Nov 2024 13:39:23 GMT
• Title: Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection
• Authors: Sepehr Nourmohammadi, Arda Sarp Yenicesu, Shervin Rahimzadeh Arashloo, Ozgur S. Oguz,
• Abstract summary: We introduce a framework that dynamically adjusts fusion weights based on local data characteristics. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications.
• Score: 17.93058599783703
• License:
• Abstract: This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency compared to traditional Frank-Wolfe approaches, achieving up to 19-fold speed improvements in complex scenarios. The framework is extensively evaluated on standard UCI benchmark datasets and specialized temporal sequence datasets, demonstrating superior performance across diverse anomaly types. Statistical validation through Skillings-Mack tests confirms our method's significant advantages over existing approaches, with consistent top rankings in both pure and non-pure learning scenarios. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications where rapid and accurate anomaly detection is crucial.

Related papers

• SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation [55.87169702896249]
  Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift. We propose a framework to evaluate DA methods and present a fair evaluation of existing shallow algorithms, including reweighting, mapping, and subspace alignment. Our benchmark highlights the importance of realistic validation and provides practical guidance for real-life applications.
  arXiv  Detail & Related papers  (2024-07-16T12:52:29Z)
• Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection [10.177917426690701]
  Hierarchical Federated Learning (HFL) faces the challenge of adversarial or unreliable vehicles in vehicular networks. Our study introduces a novel framework that integrates dynamic vehicle selection and robust anomaly detection mechanisms. Our proposed algorithm demonstrates remarkable resilience even under intense attack conditions.
  arXiv  Detail & Related papers  (2024-05-25T18:31:20Z)
• A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams [0.0]
  This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression. The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism. To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE)
  arXiv  Detail & Related papers  (2023-12-12T19:23:54Z)
• AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation [1.4530711901349282]
  We propose to validate test-time adaptation methods using datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift. We enhance the well-established self-training framework by incorporating a small memory buffer to increase model stability.
  arXiv  Detail & Related papers  (2023-09-18T19:34:23Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• Tracking Performance of Online Stochastic Learners [57.14673504239551]
  Online algorithms are popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable accuracy. We establish a link between steady-state performance derived under stationarity assumptions and the tracking performance of online learners under random walk models.
  arXiv  Detail & Related papers  (2020-04-04T14:16:27Z)
• Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization [53.99850033746663]
  We study the problem of learning localization model on target classes with weakly supervised image labels. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function.
  arXiv  Detail & Related papers  (2020-03-18T17:53:33Z)

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.