Leveraging Evidential Deep Learning Uncertainties with Graph-based
Clustering to Detect Anomalies
- URL: http://arxiv.org/abs/2107.01557v1
- Date: Sun, 4 Jul 2021 06:31:59 GMT
- Title: Leveraging Evidential Deep Learning Uncertainties with Graph-based
Clustering to Detect Anomalies
- Authors: Sandeep Kumar Singh, Jaya Shradha Fowdur, Jakob Gawlikowski and Daniel
Medina
- Abstract summary: We propose a graph-based traffic representation scheme to cluster trajectories of vessels using automatic identification system (AIS) data.
This paper proposes the usage of a deep learning (DL)-based uncertainty estimation in detecting maritime anomalies.
- Score: 1.525943491541265
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding and representing traffic patterns are key to detecting
anomalies in the maritime domain. To this end, we propose a novel graph-based
traffic representation and association scheme to cluster trajectories of
vessels using automatic identification system (AIS) data. We utilize the
(un)clustered data to train a recurrent neural network (RNN)-based evidential
regression model, which can predict a vessel's trajectory at future timesteps
with its corresponding prediction uncertainty. This paper proposes the usage of
a deep learning (DL)-based uncertainty estimation in detecting maritime
anomalies, such as unusual vessel maneuvering. Furthermore, we utilize the
evidential deep learning classifiers to detect unusual turns of vessels and the
loss of AIS signal using predicted class probabilities with associated
uncertainties. Our experimental results suggest that using graph-based
clustered data improves the ability of the DL models to learn the
temporal-spatial correlation of data and associated uncertainties. Using
different AIS datasets and experiments, we demonstrate that the estimated
prediction uncertainty yields fundamental information for the detection of
traffic anomalies in the maritime and, possibly in other domains.
Related papers
- Sequential Attention Source Identification Based on Feature
Representation [88.05527934953311]
This paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea.
It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge.
arXiv Detail & Related papers (2023-06-28T03:00:28Z) - Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps.
We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights.
In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
arXiv Detail & Related papers (2023-06-14T11:41:42Z) - ST-GIN: An Uncertainty Quantification Approach in Traffic Data
Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent
United Neural Networks [18.66289473659838]
We propose an innovative deep learning approach for imputing missing data.
A graph attention architecture is employed to capture the spatial correlations present in traffic data.
A bidirectional neural network is utilized to learn temporal information.
arXiv Detail & Related papers (2023-05-10T22:15:40Z) - Uncovering the Missing Pattern: Unified Framework Towards Trajectory
Imputation and Prediction [60.60223171143206]
Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences.
Current methods often assume that the observed sequences are complete while ignoring the potential for missing values.
This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
arXiv Detail & Related papers (2023-03-28T14:27:27Z) - Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with
Uncertainty Estimation [10.262354603266639]
In maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy.
This paper extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can be tasked to predict.
We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved.
arXiv Detail & Related papers (2022-05-11T11:01:15Z) - Uncertainty-Aware Multiple Instance Learning fromLarge-Scale Long Time
Series Data [20.2087807816461]
This paper proposes an uncertainty-aware multiple instance (MIL) framework to identify the most relevant periodautomatically.
We further incorporate another modality toaccommodate unreliable predictions by training a separate model and conduct uncertainty aware fusion.
Empirical resultsdemonstrate that the proposed method can effectively detect thetypes of vessels based on the trajectory.
arXiv Detail & Related papers (2021-11-16T17:09:02Z) - Convolutional generative adversarial imputation networks for
spatio-temporal missing data in storm surge simulations [86.5302150777089]
Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods.
We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
arXiv Detail & Related papers (2021-11-03T03:50:48Z) - Cloud Failure Prediction with Hierarchical Temporary Memory: An
Empirical Assessment [64.73243241568555]
Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex.
This paper presents the first systematic study that assesses HTM in the context of failure prediction.
arXiv Detail & Related papers (2021-10-06T07:09:45Z) - Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [17.414474298706416]
We develop a new way to detect anomalies in high-dimensional time series data.
Our approach combines a structure learning approach with graph neural networks.
We show that our method detects anomalies more accurately than baseline approaches.
arXiv Detail & Related papers (2021-06-13T09:07:30Z) - Deep Learning Methods for Vessel Trajectory Prediction based on
Recurrent Neural Networks [13.193080011901381]
We propose sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs)
The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data.
Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks.
arXiv Detail & Related papers (2021-01-07T11:05:47Z) - Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass.
We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
arXiv Detail & Related papers (2020-03-04T12:27:36Z)
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.