Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions.
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| Title: | Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions. |
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| Authors: | Fan, Wentao1 fwt@hqu.edu.cn, Yang, Lin1 19014083028@stu.hqu.edu.cn, Bouguila, Nizar2 nizar.bouguila@concordia.ca |
| Source: | IEEE Transactions on Pattern Analysis & Machine Intelligence. Dec2022, Vol. 44 Issue Part2, p9654-9668. 15p. |
| Subjects: | Data modeling, Watson (Computer), Inferential statistics, Image analysis, Mathematical optimization, Gene expression, Machine learning |
| Abstract: | This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 160711795 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fan%2C+Wentao%22">Fan, Wentao</searchLink><relatesTo>1</relatesTo><i> fwt@hqu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Lin%22">Yang, Lin</searchLink><relatesTo>1</relatesTo><i> 19014083028@stu.hqu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bouguila%2C+Nizar%22">Bouguila, Nizar</searchLink><relatesTo>2</relatesTo><i> nizar.bouguila@concordia.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Pattern+Analysis+%26+Machine+Intelligence%22">IEEE Transactions on Pattern Analysis & Machine Intelligence</searchLink>. Dec2022, Vol. 44 Issue Part2, p9654-9668. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Watson+%28Computer%29%22">Watson (Computer)</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TPAMI.2021.3128271 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 9654 Subjects: – SubjectFull: Data modeling Type: general – SubjectFull: Watson (Computer) Type: general – SubjectFull: Inferential statistics Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Gene expression Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fan, Wentao – PersonEntity: Name: NameFull: Yang, Lin – PersonEntity: Name: NameFull: Bouguila, Nizar IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 01628828 Numbering: – Type: volume Value: 44 – Type: issue Value: Part2 Titles: – TitleFull: IEEE Transactions on Pattern Analysis & Machine Intelligence Type: main |
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