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A linear-time algorithm and analysis of graph Relative Hausdorff distance [article]

Sinan G. Aksoy, Kathleen E. Nowak, Stephen J. Young
2019 arXiv   pre-print

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2019 pre-print arXiv:1903.01682v1 fatcat:eurpbdgpqjhhvi5jlyxrknum5u
In particular, we provide the first linear-time algorithm for computing RH distance, analyze examples of RH distance between pairs of real-world networks as well as structured families of graphs, and prove  ...  Graph Relative Hausdorff (RH) distance is a promising, recently proposed measure for quantifying degree distribution similarity.  ...  We would like to thank Emilie Purvine and Paul Bruillard for helpful discussions.  ... 
arXiv:1903.01682v2 fatcat:fdsktmts7rbfvlv4vuys2qja7i
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Sinan G. Aksoy, Kathleen E. Nowak, Stephen J. Young. "A linear-time algorithm and analysis of graph Relative Hausdorff distance." arXiv (2019)

Relative Hausdorff distance for network analysis

Sinan G. Aksoy, Kathleen E. Nowak, Emilie Purvine, Stephen J. Young
2019 Applied Network Science  

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The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs.  ...  In this work we study the effectiveness of RH distance as a tool for detecting anomalies in time-evolving graph sequences.  ...  Conclusions In this work, we conducted an experimental and statistical study of Relative Hausdorff distance in the context of time-evolving sequences of graphs.  ... 
doi:10.1007/s41109-019-0198-0 fatcat:z5lk7gul6vfg3e7teuyo36wiri
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Sinan G. Aksoy, Kathleen E. Nowak, Emilie Purvine, Stephen J. Young. "Relative Hausdorff distance for network analysis." Applied Network Science 4.1 (2019)

A Spot Matching Algorithm using the Topology of Neighbor Spots in 2D-PAGE Images

Chan-Myeong Han, Dae-Seong Jeoune, Hwi-Won Kim, Young-Woo Yoon
2013 International Journal of Software Engineering and Its Applications  

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In this paper, a new spot matching algorithm is proposed.  ...  Matching between neighbor spots from two patterns is performed and similarity is evaluated using the normalized Hausdorff distance(NHD).  ...  Therefore, Hausdorff distance must be normalized by dividing with pivot distance. Pivot distance(PD) is a distance between central spot p c and pivot spot p 2 .  ... 
doi:10.14257/ijseia.2013.7.5.09 fatcat:7bcc35d45ffyfmib3wzn56e5te
Citation

Chan-Myeong Han, Dae-Seong Jeoune, Hwi-Won Kim, Young-Woo Yoon. "A Spot Matching Algorithm using the Topology of Neighbor Spots in 2D-PAGE Images." International Journal of Software Engineering and Its Applications 7.5 (2013) 87-98

Pattern discovery in motion time series via structure-based spectral clustering

Xiaozhe Wang, Liang Wang, Anthony Wirth
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  

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The proposed approach deploys a 'statistical feature-based distance computation' for spectral clustering algorithm.  ...  finite set of feature vectors.  ...  Different Hausdorff-based distance measures have been evaluated and the average Hausdorff distance (the mean of the minimum values) has been argued that it outperforms other variants of Hausdorff distance  ... 
doi:10.1109/cvpr.2008.4587385 dblp:conf/cvpr/WangWW08 fatcat:4uflu4isdjch7jinnmrdulvr7q
Citation

Xiaozhe Wang, Liang Wang, Anthony Wirth. "Pattern discovery in motion time series via structure-based spectral clustering." 2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Xuyang Jing, Zheng Yan, Yinghua Shen, Witold Pedrycz, Ji Yang
2020 IEEE Transactions on Cybernetics  

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We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations.  ...  The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by using the neural network, which can explicitly provide  ...  Given two sets A and B, the Hausdorff distance between A and B involves the maximum-minimum (max-min) calculation H(A, B) = max(h(A, B), h(B, A)) (1) where h(A, B) and h(B, A) are the one-sided Hausdorff  ... 
doi:10.1109/tcyb.2020.3023373 pmid:33027030 fatcat:dw4wpg2hsjhavicceqxlioglyq
Citation

Xuyang Jing, Zheng Yan, Yinghua Shen, Witold Pedrycz, Ji Yang. "A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering." IEEE Transactions on Cybernetics (2020) 1-14

Quantification of the Variability of Continuous Glucose Monitoring Data

Edward Aboufadel, Robert Castellano, Derek Olson
2011 Algorithms  

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This wavelet-based approach emphasizes the shape of a blood glucose graph.  ...  We describe a new, wavelet-based algorithm that indicates a new measurement called a PLA index could be used to quantify the variability or predictability of blood glucose.  ...  Edward Aboufadel is a Professor of Mathematics at Grand Valley State University and served as a faculty advisor to Olson and Castellano throughout.  ... 
doi:10.3390/a4010016 fatcat:xft64cpigncu7cg7xiv5zsuhxa
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Edward Aboufadel, Robert Castellano, Derek Olson. "Quantification of the Variability of Continuous Glucose Monitoring Data." Algorithms 4.1 (2011) 16-27

A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects

Roger Alimi, Eyal Weiss, Tsuriel Ram-Cohen, Nir Geron, Idan Yogev
2015 Sensors  

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A dedicated Genetic Algorithm (GA) has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter.  ...  Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios.  ...  Conflicts of Interest The authors declare no conflict of interest.  ... 
doi:10.3390/s150923788 pmid:26393598 pmcid:PMC4610581 fatcat:mf2vys3dsvckldwjpiv2euf2p4
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Roger Alimi, Eyal Weiss, Tsuriel Ram-Cohen, Nir Geron, Idan Yogev. "A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects." Sensors 15.9 (2015) 23788-23804

The Hausdorff core problem on simple polygons

Reza Dorrigiv, Stephane Durocher, Arash Farzan, Robert Fraser, Alejandro Lopez-Ortiz, J. Ian Munro, Alejandro Salinger, Matthew Skala
2013 Journal of Computational Geometry  

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A polygon \(Q\) is a \(k\)-bounded Hausdorff Core of a polygon \(P\) if \(P\) contains \(Q\), \(Q\) is convex, and the Hausdorff distance between \(P\) and \(Q\) is at most \(k\).  ...  Given any \(k\) and any \(\varepsilon\gt 0\), we describe an algorithm for computing a \(k'\)-bounded Hausdorff Core (if one exists) in \(O(n^3+n^2\varepsilon^{-4}(\log n+ \varepsilon^{-2}))\) time, where  ...  Acknowledgements This paper has benefitted from significant improvement due to the careful reading and corrections provided by a number of anonymous reviewers as well as Thomas Erlebach, Jochen Koenemann  ... 
doi:10.20382/jocg.v5i1a2 dblp:journals/jocg/DorrigivDFFLMSS14 fatcat:asezfw6hmbelrckzilogof2iwi
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Reza Dorrigiv, Stephane Durocher, Arash Farzan, Robert Fraser, Alejandro Lopez-Ortiz, J. Ian Munro, Alejandro Salinger, Matthew Skala. "The Hausdorff core problem on simple polygons." Journal of Computational Geometry (2013) No 1 (2014)

3D early embryogenesis image filtering by nonlinear partial differential equations

Z. Krivá, K. Mikula, N. Peyriéras, B. Rizzi, A. Sarti, O. Stašová
2010 Medical Image Analysis  

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We thank all the members of the projects for our very fruitful interdisciplinary interaction.  ...  ACKNOWLEDGMENTS This work was supported by the European projects Embryomics (NEST 012916), BioEmergences (NEST 028892), Computer Vision Foundations and Applications (Alfa II-0366-FA), and grants VEGA 1  ...  of one of the nuclei from Figure 6 after 5 (a), 10 (b) and 15 (c) time steps. 20 8 Graphs of the mean Hausdorff distances for the original and filtered data using the same settings of parameters (K  ... 
doi:10.1016/j.media.2010.03.003 pmid:20457535 fatcat:sy3c7sz2djdufkolzuyi25s4zi
Citation

Z. Krivá, K. Mikula, N. Peyriéras, B. Rizzi, A. Sarti, O. Stašová. "3D early embryogenesis image filtering by nonlinear partial differential equations." Medical Image Analysis 14.4 (2010) 510-526

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy

Femke Vaassen, Colien Hazelaar, Ana Vaniqui, Mark Gooding, Brent van der Heyden, Richard Canters, Wouter van Elmpt
2020 Physics and Imaging in Radiation Oncology  

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Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = -0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and -0.64, respectively  ...  New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified  ...  For the relative contouring time saved, a correlation coefficient of 0.37 was found for the volumetric DSC, −0.63 for the Hausdorff distance, 0.55 for the surface DSC, and −0.35 for the estimated APL (  ... 
doi:10.1016/j.phro.2019.12.001 pmid:33458300 pmcid:PMC7807544 fatcat:eidquytt6ra7hk3izyd26627ju
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Femke Vaassen, Colien Hazelaar, Ana Vaniqui, Mark Gooding, Brent van der Heyden, Richard Canters, Wouter van Elmpt. "Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy." Physics and Imaging in Radiation Oncology (2020) 1-6

Efficient estimation of a Gromov–Hausdorff distance between unweighted graphs [article]

Vladyslav Oles, Nathan Lemons, Alexander Panchenko
2021 arXiv   pre-print

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2019 pre-print
arXiv:1909.09772v7 fatcat:jqgnvn6crjevjae5ui3jc4urce
2019 pre-print
arXiv:1909.09772v6 fatcat:nxd5ywjeabcxdbjo7kx3jwculy
2019 pre-print
arXiv:1909.09772v5 fatcat:blludy5c6ja5jdagppfgiarble
2019 pre-print
arXiv:1909.09772v4 fatcat:or5to2ewezbupjkgobptyv33ii
2019 pre-print
arXiv:1909.09772v3 fatcat:4bav2n6o55d2tgikbaqlcqiywi
2019 pre-print
arXiv:1909.09772v2 fatcat:zwdiiecmczgtpmpxydoqyofuji
2019 pre-print
arXiv:1909.09772v1 fatcat:f2hdmxe34zaeraiqv6v45sqk3i
We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library , and demonstrate its performance on real-world and synthetic networks.  ...  The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.  ...  While polynomial-time approximation of the GH distance was implemented for trees in [AFN+ 18], this is the first time a practical algorithm for a Gromov–Hausdorff distance is given for a broad class of  ... 
arXiv:1909.09772v8 fatcat:w5jmdinvbbanpijv7osqpr26ue
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Vladyslav Oles, Nathan Lemons, Alexander Panchenko. "Efficient estimation of a Gromov–Hausdorff distance between unweighted graphs." arXiv (2021)

Interactive Hausdorff distance computation for general polygonal models

Min Tang, Minkyoung Lee, Young J. Kim
2009 ACM Transactions on Graphics  

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This algorithm can approximate the Hausdorff distance between polygonal models consisting of tens of thousands triangles with a small error bound in real-time, and outperforms the existing algorithm by  ...  To avoid the high computational and implementation complexity of exact Hausdorff distance calculation, we approximate the Hausdorff distance within a user-specified error bound.  ...  We thank Michael Guthe for sharing his Hausdorff distance codes with us.  ... 
doi:10.1145/1531326.1531380 fatcat:sejxy55o6zehjd2qrito5zir5i
Citation

Min Tang, Minkyoung Lee, Young J. Kim. "Interactive Hausdorff distance computation for general polygonal models." ACM Transactions on Graphics 28.3 (2009)

Interactive Hausdorff distance computation for general polygonal models

Min Tang, Minkyoung Lee, Young J. Kim
2009 ACM SIGGRAPH 2009 papers on - SIGGRAPH '09  

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This algorithm can approximate the Hausdorff distance between polygonal models consisting of tens of thousands triangles with a small error bound in real-time, and outperforms the existing algorithm by  ...  To avoid the high computational and implementation complexity of exact Hausdorff distance calculation, we approximate the Hausdorff distance within a user-specified error bound.  ...  We thank Michael Guthe for sharing his Hausdorff distance codes with us.  ... 
doi:10.1145/1576246.1531380 fatcat:p5fgkx2etjb2dbxeuutcbpo4ue
Citation

Min Tang, Minkyoung Lee, Young J. Kim. "Interactive Hausdorff distance computation for general polygonal models." ACM SIGGRAPH 2009 papers on - SIGGRAPH '09 (2009)

Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process [chapter]

Olympia Roeva, Stefka Fidanov
2012 Real-World Applications of Genetic Algorithms  

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and metaheuristics methods DMU 02/4 and TK-Effective Monte Carlo Methods for large-scale scientific problems DTK 02/44.  ...  Acknowledgements This work has been partially supported by the Bulgarian National Scientific Fund under the grants High quality control of biotechnological processes with application of modified conventional  ...  The objective function is a sum of the modified Hausdorff distance between the modeled and measured data of the biomass and substrate.  ... 
doi:10.5772/36662 fatcat:ak3b2qvdfvh7tpyvok3tgxqia4
Citation

Olympia Roeva, Stefka Fidanov. "Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process." Real-World Applications of Genetic Algorithms (2012)

Visual Tools for Analysing Evolution, Emergence, and Error in Data Streams

Sol Hart, John Yearwood, Adil M. Bagirov
2007 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)  

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The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities.  ...  In this paper, we present two new and novel visulisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional  ...  Global K-means has a similar time order of complexity to that of K-means of O(nk), with k being the number of centers. It has a linear space complexity of O(n).  ... 
doi:10.1109/icis.2007.194 dblp:conf/ACISicis/HartYB07 fatcat:tdjgqf7n7zgbjc7tevccb2oojm
Citation

Sol Hart, John Yearwood, Adil M. Bagirov. "Visual Tools for Analysing Evolution, Emergence, and Error in Data Streams." 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007) (2007) 987-992

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