Graph Anomaly Detection Using Dictionary Learning
نویسندگان
چکیده
منابع مشابه
Graph regularized seismic dictionary learning
A graph-based regularization for geophysical inversion is proposed that offers a more efficient way to solve inverse denoising problems by dictionary learning methods designed to find a sparse signal representation that adaptively captures prominent characteristics in a given data. Most traditional dictionary learning methods convert 2D seismic data patches or 3D data volumes into 1D vectors fo...
متن کاملMulti-Scale Saliency Detection using Dictionary Learning
Saliency detection has drawn a lot of attention of researchers in various fields over the past several years. Saliency is the perceptual quality that makes an object, person to draw the attention of humans at the very sight. Salient object detection in an image has been used centrally in many computational photography and computer vision applications like video compression [1], object recogniti...
متن کاملWormhole Detection using Topology Graph based Anomaly Detection (TOGBAD)
Routing Attacks are a serious threat to communication in tactical MANETs. TOGBAD is a centralised approach, using topology graphs to detect such attacks. In this paper, we present TOGBAD’s newly added wormhole detection capability. It is an adaptation of a wormhole detection method developed by Hu et al. This method is based on nodes’ positions. We adapted it to the specific properties of tacti...
متن کاملEnvironmental Sensor Anomaly Detection Using Learning Machines
Environmental Sensor Anomaly Detection Using Learning Machines
متن کاملGraph-based Image Anomaly Detection
RX Detector is recognized as the benchmark algorithm for image anomaly detection, however it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a highly dimensional covariance matrix and the inability to effectively include spatial awareness in its evaluation. In this work a novel graph-based solution to the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2020
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2020.12.1731