☆ 4.7 Article

Ensemble Gaussian Processes for Online Learning Over Graphs With Adaptivity and Scalability

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Geochemistry & Geophysics

Hyperspectral Image Classification Based on Deep Attention Graph Convolutional Network

Jing Bai et al.

Summary: This paper proposes a framework based on a deep attention graph convolutional network (DAGCN) to address the challenges of hyperspectral image classification. By integrating attention mechanism and designing deep graph convolutional networks, deep abstract features are extracted and the internal relationship between HSI data is explored, achieving superior classification results compared to the baselines.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Comprehensive Survey on Graph Neural Networks

Zonghan Wu et al.

Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference

Michael E. Kepler et al.

Summary: The method overcomes scalability issues of Gaussian processes for large sample sizes and performance degradation for non-stationary or spatially heterogeneous data by utilizing variational free energy approximations and online expectation propagation steps. Introducing a local splitting step creates an ensemble of sparse GPs that adapt to the data over time. Compared to other Gaussian process regression methods, this approach often achieves competitive or superior predictive performance.

2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2021)

添加到收藏夹

Proceedings Paper Acoustics

GRAPH-ADAPTIVE INCREMENTAL LEARNING USING AN ENSEMBLE OF GAUSSIAN PROCESS EXPERTS

Konstantinos D. Polyzos et al.

Summary: This study addresses the graph-guided semi-supervised learning task in a Bayesian framework based on Gaussian processes to propagate the distribution of nonparametric function estimates. By utilizing random features for scalability and employing an ensemble of GP experts to choose the fitted kernel combination in a graph- and data-adaptive fashion, the need for offline model training is bypassed.

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (2021)

添加到收藏夹

Proceedings Paper Acoustics

ONLINE UNSUPERVISED LEARNING USING ENSEMBLE GAUSSIAN PROCESSES WITH RANDOM FEATURES

Georgios Karanikolas et al.

Summary: The proposed method is an efficient online approach based on random features, optimizing computational efficiency by replacing spatial subsampling. Unlike GPLVM, this algorithm relies on an ensemble of kernels, allowing adaptation to different operating environments and initial exploration in a richer function space. Tests on benchmark datasets demonstrate the effectiveness of the method.

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Information Systems

Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks

Konstantinos D. Polyzos et al.

2020 54TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Graph-Adaptive Semi-Supervised Tracking of Dynamic Processes Over Switching Network Modes

Qin Lu et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Online Graph-Adaptive Learning With Scalability and Privacy

Yanning Shen et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Topology Identification and Learning Over Graphs: Accounting for Nonlinearities and Dynamics

Georgios B. Giannakis et al.

PROCEEDINGS OF THE IEEE (2018)

添加到收藏夹

Article Engineering, Electrical & Electronic

Identification of Overlapping Communities via Constrained Egonet Tensor Decomposition

Fatemeh Sheikholeslami et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2018)

添加到收藏夹

Article Engineering, Electrical & Electronic

Kernel-Based Reconstruction of Graph Signals

Daniel Romero et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2017)

添加到收藏夹

Article Engineering, Electrical & Electronic

Adaptive Least Mean Squares Estimation of Graph Signals

Paolo Di Lorenzo et al.

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS (2016)

添加到收藏夹

Article Engineering, Electrical & Electronic

Prediction of Partially Observed Dynamical Processes Over Networks via Dictionary Learning

Pedro A. Forero et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2014)

添加到收藏夹

Article Engineering, Electrical & Electronic

The Emerging Field of Signal Processing on Graphs

David I. Shuman et al.

IEEE SIGNAL PROCESSING MAGAZINE (2013)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

Arjan Gijsberts et al.

NEURAL NETWORKS (2013)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.