Hyperspectral and mutispectral image fusion via coupled block term decomposition with graph Laplacian regularization

2021 ◽  
Author(s):  
Wen Jiang ◽  
Hongyi Liu ◽  
Jun Zhang
Keyword(s):  
Image Fusion ◽  
Graph Laplacian ◽  
Laplacian Regularization ◽  
Block Term Decomposition

scholarly journals Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6628
Author(s):  
Kanghang He ◽  
Vladimir Stankovic ◽  
Lina Stankovic
Keyword(s):  
Dynamic Time Warping ◽  
Graph Laplacian ◽  
Classification Performance ◽  
Time Warping ◽  
Load Monitoring ◽  
Segmentation Approach ◽  
Length Data ◽  
Labeling Approach ◽  
Dynamic Time ◽  
Laplacian Regularization

Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-length data segmentation approach to extract potential events, assign all measurements associated with an identified event to each graph node, employ dynamic time warping to define the adjacency matrix of the graph, and propose a robust cluster labeling approach. Our simulation results on four different datasets show up to 10% improvement in classification performance over competing approaches.

Color-Guided Depth Image Recovery With Adaptive Data Fidelity and Transferred Graph Laplacian Regularization

2020 ◽  
Vol 30 (2) ◽  
pp. 320-333 ◽  
Author(s):  
Yongbing Zhang ◽  
Yihui Feng ◽  
Xianming Liu ◽  
Deming Zhai ◽  
Xiangyang Ji ◽  
...  
Keyword(s):  
Graph Laplacian ◽  
Depth Image ◽  
Image Recovery ◽  
Data Fidelity ◽  
Laplacian Regularization

scholarly journals Graph Transduction as a Noncooperative Game

2012 ◽  
Vol 24 (3) ◽  
pp. 700-723 ◽  
Author(s):  
Aykut Erdem ◽  
Marcello Pelillo
Keyword(s):  
Graph Laplacian ◽  
Semisupervised Learning ◽  
Label Propagation ◽  
General Idea ◽  
Noncooperative Game ◽  
Similarity Relations ◽  
Label Information ◽  
Data Points ◽  
Game Theoretic ◽  
Laplacian Regularization

Graph transduction is a popular class of semisupervised learning techniques that aims to estimate a classification function defined over a graph of labeled and unlabeled data points. The general idea is to propagate the provided label information to unlabeled nodes in a consistent way. In contrast to the traditional view, in which the process of label propagation is defined as a graph Laplacian regularization, this article proposes a radically different perspective, based on game-theoretic notions. Within the proposed framework, the transduction problem is formulated in terms of a noncooperative multiplayer game whereby equilibria correspond to consistent labelings of the data. An attractive feature of this formulation is that it is inherently a multiclass approach and imposes no constraint whatsoever on the structure of the pairwise similarity matrix, being able to naturally deal with asymmetric and negative similarities alike. Experiments on a number of real-world problems demonstrate that the proposed approach performs well compared with state-of-the-art algorithms, and it can deal effectively with various types of similarity relations.

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