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Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings [article]

Daniele Zambon, Lorenzo Livi, Cesare Alippi
2018 pre-print
Here, we investigate how embedding graphs on constant-curvature manifolds (hyper-spherical and hyperbolic manifolds) impacts on the ability to detect changes in sequences of attributed graphs.  ...  The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams.  ...  Acknowledgements This research is funded by the Swiss National Science Foundation project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems".  ... 
doi:10.1109/ijcnn.2018.8489762 arXiv:1805.01360v1 fatcat:5ca2iz6ee5cpnlf2apk4qdv63m

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds [article]

Daniele Grattarola, Daniele Zambon, Cesare Alippi, Lorenzo Livi
2019 arXiv   pre-print
In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs.  ...  Among these, constant-curvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric  ...  Acknowledgements This research is funded by the Swiss National Science Foundation project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems".  ... 
arXiv:1805.06299v3 fatcat:v3xpdicj3zg6higeso2gwmij4u

Unsupervised Detection of Changes in Usage-Phases of a Mobile App

Hoyeol Chae, Ryangkyung Kang, Ho-Sik Seok
2020 Applied Sciences  
Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models.  ...  This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app.  ...  In order to represent graphs as points in a non-Euclidean space and detect changes, adversarial training of autoencoders was employed for graph embeddings on constant-curvature manifolds, and change detection  ... 
doi:10.3390/app10103656 fatcat:us5frw3hwncplj7gfhli7pgxze

Nonlinear Dimensionality Reduction Methods in Climate Data Analysis [article]

Ian Ross
2009 arXiv   pre-print
Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets  ...  The three methods used here are a nonlinear principal component analysis (NLPCA) approach based on neural networks, the Isomap isometric mapping algorithm, and Hessian locally linear embedding.  ...  it uses a nearest neighbour graph and shortest paths through the graph to define approximate geodesics in the data manifold.  ... 
arXiv:0901.0537v1 fatcat:2ethc7ddtjdyxkbqzmtg64upwi

Anomaly Detection in Big Data [article]

Chandresh Kumar Maurya
2022 arXiv   pre-print
Therefore, we take an alternative approach to tackle anomaly detection in big data. Essentially, there are two ways to scale anomaly detection in big data.  ...  The first is based on the online learning and the second is based on the distributed learning. Our aim in the thesis is to tackle big data problems while detecting anomaly efficiently.  ...  Fast anomaly detection using Half-Space Trees was proposed in [137] . Their Streaming HS-Tree algorithm has constant amortized complexity of O(1) and constant space complexity of O(1).  ... 
arXiv:2203.01684v1 fatcat:3w5yogrqwnasdn3niubj2pgghi

Multiple Infrared Small Targets Detection based on Hierarchical Maximal Entropy Random Walk [article]

Chaoqun Xia, Xiaorun Li, Liaoying Zhao, Shuhan Chen
2020 arXiv   pre-print
In this paper, we establish a detection method derived from maximal entropy random walk (MERW) to robustly detect multiple small targets.  ...  The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is very important for infrared search and tracking systems.  ...  (a) A synthetic manifold data set with 10000 nodes and 3 anomalies. (b) One-dimensional embedding for the synthetic data set obtained by (5) .  ... 
arXiv:2010.00923v1 fatcat:hadoqmtndje3xl3jmiz7iz2acq

Towards a fourth spatial dimension of brain activity

Arturo Tozzi, James F. Peters
2016 Cognitive Neurodynamics  
This manuscript encompasses our published and unpublished topological results in neuroscience.  ...  Topology, the mathematical branch that assesses objects and their properties preserved through deformations, stretching and twisting, allows the investigation of the most general brain features.  ...  We can thus evaluate how changes of Rényi parameter influence the structure of information measures in the probability space (Figure 6 ).  ... 
doi:10.1007/s11571-016-9379-z pmid:27275375 pmcid:PMC4870410 fatcat:kgfvl25o6fcpfdiitcmvaumska

Survey of Generative Methods for Social Media Analysis [article]

Stan Matwin, Aristides Milios, Paweł Prałat, Amilcar Soares, François Théberge
2021 arXiv   pre-print
We included two important aspects that currently gain importance in mining and modeling social media: dynamics and networks.  ...  It fills a void, as the existing survey articles are either much narrower in their scope or are dated.  ...  There are several other methods for graph-based anomaly detection that could be possibly investigated; see for example [4] and [185] .  ... 
arXiv:2112.07041v1 fatcat:xgmduwctpbddfo67y6ack5s2um

Local Model Feature Transformations [article]

CScott Brown
2020 arXiv   pre-print
In this document, we extend the local modeling paradigm to Gaussian processes, orthogonal quadric models and word embedding models, and extend the existing theory for localized linear classifiers.  ...  event detection from Twitter feeds.  ...  The discovery of these variables and their effects are often the subject of unsupervised learning methods such as clustering, anomaly/outlier detection and change point detection.  ... 
arXiv:2004.06149v1 fatcat:mgosv4hjabbj7esu7ezdnr3sl4

Microcanonical multifractal formalism: Application to the estimation of ocean surface velocities

J. Isern-Fontanet, A. Turiel, E. García-Ladona, J. Font
2007 Journal of Geophysical Research  
As multifractal analysis is in essence a geometrical approach, the method is able to retrieve a high resolution velocity field, well localized in space, but with some indetermination on the modulus and  ...  The most probable origin of the observed structures is the turbulent character of the oceanic flow as they evolve slowly and are very persistent in times compatible with ocean mesoscale dynamics (several  ...  This is a contribution to the EU MERSEA project (AIP3- CT-2003-502885) and to the Spanish projects ESEOO (VEM2003-20577-C14-10) and MIDAS-4 (ESP2005-06823-C05-1). A.  ... 
doi:10.1029/2006jc003878 fatcat:6efiqmpiareyhcvlee62uvczmm

Table of contents

2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
SMOOTH SPARSE ........................................... 5170 DECOMPOSITION FOR ANOMALY DETECTION IN SPATIOTEMPORAL DATA Seyyid Emre Sofuoglu, Selin Aviyente, Michigan State University, United States  ...  LEARNING Nakamasa Inoue, Tokyo Institute of Technology, Japan MMSP-2.4: RULE-EMBEDDED NETWORK FOR AUDIO-VISUAL VOICE ACTIVITY ...................................... 4165 DETECTION IN LIVE MUSICAL VIDEO  ... 
doi:10.1109/icassp39728.2021.9414617 fatcat:m5ugnnuk7nacbd6jr6gv2lsfby

Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study

Nawaf Alsrehin, Ahmad F. Klaib, Aws Magableh
2019 IEEE Access  
This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies  ...  The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only.  ...  Using up to date urban anomaly data, the UAPD first detects the change point of each type of anomalies in the temporal dimension and then uses a tensor decomposition model to decouple the interrelations  ... 
doi:10.1109/access.2019.2909114 fatcat:k3kbfxezdvhihgg2g76vv5xxqq

Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences [article]

J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay, Miguel D. Mahecha, Gustau Camps-Valls
2020 arXiv   pre-print
Moreover we provide intuitive explanations through illustrative toy examples and show how to improve the interpretation of real applications in the context of spatiotemporal Earth system data cubes.  ...  They Have a solid mathematical background and exhibit excellent performance in practice.  ...  of potential regions of interest due to their location in the PDF, which could be related to anomalies, and 4) we show that we can detect changes in dependence between two events during an extreme heatwave  ... 
arXiv:2007.14706v2 fatcat:h4mcvkyrd5derm4kfnlloaziky

Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies [article]

Elcio Abdalla, Guillermo Franco Abellán, Amin Aboubrahim, Adriano Agnello, Ozgur Akarsu, Yashar Akrami, George Alestas, Daniel Aloni, Luca Amendola, Luis A. Anchordoqui, Richard I. Anderson, Nikki Arendse (+191 others)
2022 arXiv   pre-print
in the value of the Hubble constant H_0, the σ_8–S_8 tension, and other less statistically significant anomalies.  ...  In this paper, we focus on the 5.0 σ tension between the Planck CMB estimate of the Hubble constant H_0 and the SH0ES collaboration measurements.  ...  Therefore, the constant-curvature degree of freedom of the standard model is not sufficient to explain the tensions and anomalies seen in the data.  ... 
arXiv:2203.06142v3 fatcat:qt4pvdb5m5abtknosue6kphxpe

Special issue on information reuse and integration

2007 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
In this study, we identify learners (from a total of 11 classification algorithms) with robust performance in the presence of low quality imbalanced measurement data.  ...  In most scenarios, the actual quality of such datasets is unknown to the data mining practitioner.  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of AFRL or the  ... 
doi:10.1109/tsmcb.2007.912701 fatcat:xvhlaf4m3vhcdb2cicqtfrug6m
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