A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering

dc.contributor.authorQin, Yu
dc.contributor.authorFasy, Brittany Terese
dc.contributor.authorWenk, Carola
dc.contributor.authorSumma, Brian
dc.date.accessioned2022-09-28T20:44:01Z
dc.date.available2022-09-28T20:44:01Z
dc.date.issued2021-01
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractPersistence diagrams have been widely used to quantify the underlying features of filtered topological spaces in data visualization. In many applications, computing distances between diagrams is essential; however, computing these distances has been challenging due to the computational cost. In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances. This framework is built upon a generative adversarial network (GAN) with a diagram distance loss function to steer the learning process. Instead of using standard representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks. The training of this model is domain-oblivious in that it can be computed purely from synthetic, randomly created diagrams. As a consequence, our proposed method is directly applicable to various datasets without the need for retraining the model. These binary codes, when compared using fast Hamming distance, better maintain topological similarity properties between datasets than other vectorized representations. To evaluate this method, we apply our framework to the problem of diagram clustering and we compare the quality and performance of our approach to the state-of-the-art. In addition, we show the scalability of our approach on a dataset with 10k persistence diagrams, which is not possible with current techniques. Moreover, our experimental results demonstrate that our method is significantly faster with the potential of less memory usage, while retaining comparable or better quality comparisons.en_US
dc.identifier.citationQin, Y., Fasy, B. T., Wenk, C., & Summa, B. (2021). A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering. IEEE Transactions on Visualization and Computer Graphics, 28(1), 302-312.en_US
dc.identifier.issn1077-2626
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/17238
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightscopyright Institute of Electrical and Electronics Engineers 2021en_US
dc.rights.urihttps://web.archive.org/web/20200608035444/https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/en_US
dc.subjecttopological data analysisen_US
dc.subjectpersistence diagramsen_US
dc.subjectpersistence diagram distancesen_US
dc.subjectlearned hashingen_US
dc.subjectclusteringen_US
dc.titleA Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clusteringen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage11en_US
mus.citation.issue1en_US
mus.citation.journaltitleIEEE Transactions on Visualization and Computer Graphicsen_US
mus.citation.volume28en_US
mus.data.thumbpage7en_US
mus.identifier.doi10.1109/TVCG.2021.3114872en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
qin-topological-2021.pdf
Size:
1.21 MB
Format:
Adobe Portable Document Format
Description:
topological spaces
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
826 B
Format:
Item-specific license agreed upon to submission
Description:
Copyright (c) 2002-2022, LYRASIS. All rights reserved.