Related references
Note: Only part of the references are listed.Sequential subspace change point detection
Liyan Xie et al.
SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS (2020)
Change detection in noisy dynamic networks: a spectral embedding approach
Isuru Udayangani Hewapathirana et al.
SOCIAL NETWORK ANALYSIS AND MINING (2020)
Fast detection of community structures using graph traversal in social networks
Partha Basuchowdhuri et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2019)
Optimal change point detection in Gaussian processes
Hossein Keshavarz et al.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE (2018)
SPOTLIGHT: Detecting Anomalies in Streaming Graphs
Dhivya Eswaran et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)
Anatomy of Old Faithful From Subsurface Seismic Imaging of the Yellowstone Upper Geyser Basin
Sin-Mei Wu et al.
GEOPHYSICAL RESEARCH LETTERS (2017)
TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
Han Liu et al.
ELECTRONIC JOURNAL OF STATISTICS (2017)
The Yellowstone magmatic system from the mantle plume to the upper crust
Hsin-Hua Huang et al.
SCIENCE (2015)
STRUCTURE ESTIMATION FOR DISCRETE GRAPHICAL MODELS: GENERALIZED COVARIANCE MATRICES AND THEIR INVERSES
Po-Ling Loh et al.
ANNALS OF STATISTICS (2013)
Change-Point Detection for High-Dimensional Time Series With Missing Data
Yao Xie et al.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2013)
Forecasting large aftershocks within one day after the main shock
Takahiro Omi et al.
SCIENTIFIC REPORTS (2013)
Community detection in graphs
Santo Fortunato
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2010)
A tutorial on spectral clustering
Ulrike von Luxburg
STATISTICS AND COMPUTING (2007)
Model selection and estimation in the Gaussian graphical model
Ming Yuan et al.
BIOMETRIKA (2007)
Quickest detection of a random signal in background noise using a sensor array
T Oskiper et al.
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING (2005)
Share Paper
