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Found 7 papers, 1 papers with code
GraphSAC: Detecting anomalies in large-scale graphs
Alleviating this limitation, GraphSAC randomly draws subsets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employing a semi-supervised learning (SSL) module to estimate nominal label distributions per node.
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs
Moreover, an algorithmic scheme is proposed for training the model parameters effieciently and in an unsupervised manner.
Adaptive Bayesian Radio Tomography
Key to success of RTI is to model accurately the shadowing effects as the bi-dimensional integral of the SLF scaled by a weight function, which is estimated using regularized regression.
Signal Processing Applications
Adaptive Diffusions for Scalable Learning over Graphs
Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements.
Data-adaptive Active Sampling for Efficient Graph-Cognizant Classification
Leveraging the graph for classification builds on the premise that labels across neighboring nodes are correlated according to a categorical Markov random field (MRF).
Large-scale Kernel-based Feature Extraction via Budgeted Nonlinear Subspace Tracking
Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks.
Online Censoring for Large-Scale Regressions with Application to Streaming Big Data
Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability.
