Multi-view Anomaly Detection via Probabilistic Latent Variable Models
We propose a nonparametric Bayesian probabilistic latent variable model for multi-view anomaly detection, which is the task of finding instances that have inconsistent views. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anomalous instance is assumed to have multiple latent vectors, and its different views are generated from different latent vectors. By inferring the number of latent vectors used for each instance with Dirichlet process priors, we obtain multi-view anomaly scores. The proposed model can be seen as a robust extension of probabilistic canonical correlation analysis for noisy multi-view data. We present Bayesian inference procedures for the proposed model based on a stochastic EM algorithm. The effectiveness of the proposed model is demonstrated in terms of performance when detecting multi-view anomalies and imputing missing values in multi-view data with anomalies.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionBayesian InferenceMissing ValuesSimilar Papers 제목 키워드 기반
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
We propose probabilistic latent variable models for multi-view anomaly detection, which is the task of finding instances that have inconsistent views given multi-view data. With the proposed model, all views of a non-ano…
Anomaly DetectionBayesian InferenceAnomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network
In this paper, an unsupervised Recurrent Wavelet Probabilistic Neural Network (RWPNN) is proposed, which aims at detecting anomalies in non-stationary environments by modelling the temporal features using a nonparametric…
Anomaly DetectionDecoderDensity EstimationTime Series+1Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection
Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative …
Anomaly DetectionVELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection
Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods. In view …
Anomaly DetectionDecoderTime SeriesTime Series Analysis+1Kidney Cancer Detection Using 3D-Based Latent Diffusion Models
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusio…
Supervised Anomaly Detection