scholarly journals Superpixel Graph Label Transfer with Learned Distance Metric

2014 ◽  
pp. 632-647 ◽  
Author(s):  
Stephen Gould ◽  
Jiecheng Zhao ◽  
Xuming He ◽  
Yuhang Zhang
Keyword(s):  
Distance Metric ◽  
Label Transfer ◽  
Graph Label

Contextual-distance Metric Based Laplacian Maximum Margin Criterion

2011 ◽  
Vol 36 (12) ◽  
pp. 1661-1673
Author(s):  
Jun GAO ◽  
Shi-Tong WANG ◽  
Xiao-Ming WANG
Keyword(s):  
Distance Metric ◽  
Maximum Margin ◽  
Maximum Margin Criterion

scholarly journals Distance metric learning for graph structured data

2021 ◽  
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Metric Learning ◽  
Structured Data ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Betweenness centrality for temporal multiplexes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Silvia Zaoli ◽  
Piero Mazzarisi ◽  
Fabrizio Lillo
Keyword(s):  
Information Flow ◽  
Real World ◽  
Betweenness Centrality ◽  
Temporal Structure ◽  
Shortest Paths ◽  
Single Layer ◽  
Distance Metric ◽  
Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

2012 ◽  
Vol 22 (5) ◽  
pp. 1013-1022 ◽  
Author(s):  
D. Jude Hemanth ◽  
C. Kezi Selva Vijila ◽  
A. Immanuel Selvakumar ◽  
J. Anitha
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Distance Metric ◽  
Fuzzy Algorithm ◽  
Brain Image ◽  
Brain Image Segmentation
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