Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection

• URL: http://arxiv.org/abs/2404.04064v1
• Date: Fri, 5 Apr 2024 12:41:53 GMT
• Title: Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
• Authors: Paul Irofti, Iulian-Andrei Hîji, Andrei Pătraşcu, Nicolae Cleju,
• Abstract summary: We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective. We extend both objectives to the more general setting that allows the use of kernel functions.
• Score: 1.5999407512883508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study in this paper the improvement of one-class support vector machines (OC-SVM) through sparse representation techniques for unsupervised anomaly detection. As Dictionary Learning (DL) became recently a common analysis technique that reveals hidden sparse patterns of data, our approach uses this insight to endow unsupervised detection with more control on pattern finding and dimensions. We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms. A closed-form of the alternating K-SVD iteration is explicitly derived for the new composite model and practical implementable schemes are discussed. The standard DL model is adapted for the Dictionary Pair Learning (DPL) context, where the usual sparsity constraints are naturally eliminated. Finally, we extend both objectives to the more general setting that allows the use of kernel functions. The empirical convergence properties of the resulting algorithms are provided and an in-depth analysis of their parametrization is performed while also demonstrating their numerical performance in comparison with existing methods.

Related papers

• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Supervised Dictionary Learning with Auxiliary Covariates [0.0]
  Supervised dictionary learning (SDL) is a machine learning method that simultaneously seeks feature extraction and classification tasks. We provide a novel framework that lifts' SDL as a convex problem in a combined factor space. We apply SDL for imbalanced document classification by supervised topic modeling and also for pneumonia from chest X-ray images.
  arXiv  Detail & Related papers  (2022-06-14T12:10:03Z)
• Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection [14.721615285883429]
  collaborative representation-based (CR) algorithms have been proposed for hyperspectral anomaly detection. CR-based detectors approximate the image by a linear combination of background dictionaries and the coefficient matrix, and derive the detection map by utilizing recovery residuals. This paper proposes a nonnegative-constrained joint collaborative representation model for the hyperspectral anomaly detection task.
  arXiv  Detail & Related papers  (2022-03-18T16:02:27Z)
• DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection [9.19194451963411]
  Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. We propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation.
  arXiv  Detail & Related papers  (2021-06-09T21:57:41Z)
• A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings [50.524054820564395]
  We propose a new unsupervised model for mapping a variable-duration speech segment to a fixed-dimensional representation. The resulting acoustic word embeddings can form the basis of search, discovery, and indexing systems for low- and zero-resource languages.
  arXiv  Detail & Related papers  (2020-12-03T19:24:42Z)
• CASTLE: Regularization via Auxiliary Causal Graph Discovery [89.74800176981842]
  We introduce Causal Structure Learning (CASTLE) regularization and propose to regularize a neural network by jointly learning the causal relationships between variables. CASTLE efficiently reconstructs only the features in the causal DAG that have a causal neighbor, whereas reconstruction-based regularizers suboptimally reconstruct all input features.
  arXiv  Detail & Related papers  (2020-09-28T09:49:38Z)
• Dual Adversarial Auto-Encoders for Clustering [152.84443014554745]
  We propose Dual Adversarial Auto-encoder (Dual-AAE) for unsupervised clustering. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2020-08-23T13:16:34Z)
• Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection [5.491655566898372]
  We build a scalable machine learning system for unsupervised anomaly detection via representation learning. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error. We show empirically the competitive performance of our approach on benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-05T00:03:48Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.