A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
[article]
2018
pre-print
Preserved Fulltext
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2020
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2009
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2016
Cognitive Neurodynamics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2007
Journal of Geophysical Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 213 results