Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

• URL: http://arxiv.org/abs/2203.00320v1
• Date: Tue, 1 Mar 2022 09:50:43 GMT
• Title: Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise
• Authors: Yi Yan, Radwa Adel, Ercan Engin Kuruoglu
• Abstract summary: We introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) The GNLMP algorithm has the ability to reconstruct a graph signal corrupted by non-Gaussian noise with heavy-tailed characteristics. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.
• Score: 1.1279808969568252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from least-squares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated as well. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.

Related papers

• Partitioning Message Passing for Graph Fraud Detection [57.928658584067556]
  Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily. In our work, we argue that the key to applying GNNs for GFD is not to exclude but to em distinguish neighbors with different labels.
  arXiv  Detail & Related papers  (2024-11-16T11:30:53Z)
• Adaptive Least Mean pth Power Graph Neural Networks [5.4004917284050835]
  We propose a universal framework combining adaptive filter and graph neural network for online graph signal estimation. LMP-GNN retains the advantage of adaptive filtering in handling noise and missing observations as well as the online update capability. Experiment results on two real-world datasets of temperature graph and traffic graph under four different noise distributions prove the effectiveness and robustness of our proposed LMP-GNN.
  arXiv  Detail & Related papers  (2024-05-07T08:28:51Z)
• Compressive Recovery of Signals Defined on Perturbed Graphs [4.021249101488848]
  We present an algorithm which simultaneously recovers the signal from the compressive measurements and also corrects the graph perturbations. An application to compressive image reconstruction is also presented, where graph perturbations are modeled as undesirable graph edges linking pixels with significant intensity difference.
  arXiv  Detail & Related papers  (2024-02-12T13:20:52Z)
• ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network [20.924559944655392]
  This paper proposes an Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network (ASWT-SGNN) ASWT-SGNN accurately approximates the filter function in high-density spectral regions, avoiding costly eigen-decomposition. It achieves comparable performance to state-of-the-art models in node classification tasks.
  arXiv  Detail & Related papers  (2023-12-10T03:07:42Z)
• Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation [3.6448362316632115]
  Leveraging the Line Graph transform, the Line Graph Least Mean Square (LGLMS) algorithm is proposed to conduct adaptive estimation of time-varying edge signals. LGLMS is an adaptive algorithm analogous to the classical LMS algorithm but applied to graph edges.
  arXiv  Detail & Related papers  (2023-11-01T17:02:41Z)
• Learnable Filters for Geometric Scattering Modules [64.03877398967282]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2022-08-15T22:30:07Z)
• Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals [8.140698535149042]
  We propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix. We show that the proposed algorithm converges faster, in terms of the average number of iterations, than several existing methods in the literature.
  arXiv  Detail & Related papers  (2022-02-06T17:06:13Z)
• Unrolling of Deep Graph Total Variation for Image Denoising [106.93258903150702]
  In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design. We employ interpretable analytical low-pass graph filters and employ 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN.
  arXiv  Detail & Related papers  (2020-10-21T20:04:22Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification [70.50751397870972]
  FastGAT is a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks.
  arXiv  Detail & Related papers  (2020-06-15T22:07:54Z)
• Graphon Pooling in Graph Neural Networks [169.09536309161314]
  Graph neural networks (GNNs) have been used effectively in different applications involving the processing of signals on irregular structures modeled by graphs. We propose a new strategy for pooling and sampling on GNNs using graphons which preserves the spectral properties of the graph.
  arXiv  Detail & Related papers  (2020-03-03T21:04:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.