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Euclidean Embedding of Co-occurrence Data

Published: 01 December 2007 Publication History

Abstract

Embedding algorithms search for a low dimensional continuous representation of data, but most algorithms only handle objects of a single type for which pairwise distances are specified. This paper describes a method for embedding objects of different types, such as images and text, into a single common Euclidean space, based on their co-occurrence statistics. The joint distributions are modeled as exponentials of Euclidean distances in the low-dimensional embedding space, which links the problem to convex optimization over positive semidefinite matrices. The local structure of the embedding corresponds to the statistical correlations via random walks in the Euclidean space. We quantify the performance of our method on two text data sets, and show that it consistently and significantly outperforms standard methods of statistical correspondence modeling, such as multidimensional scaling, IsoMap and correspondence analysis.

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  • (2022)Dynamic Gaussian Embedding of AuthorsProceedings of the ACM Web Conference 202210.1145/3485447.3512084(2109-2119)Online publication date: 25-Apr-2022
  • (2022)Efficient Processing of Sparse Tensor Decomposition via Unified Abstraction and PE-Interactive ArchitectureIEEE Transactions on Computers10.1109/TC.2020.304661771:2(266-281)Online publication date: 1-Feb-2022
  • (2022)A Krylov-Schur-like method for computing the best rank-(r1,r2,r3) approximation of large and sparse tensorsNumerical Algorithms10.1007/s11075-022-01303-091:3(1315-1347)Online publication date: 1-Nov-2022
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Published In

cover image The Journal of Machine Learning Research
The Journal of Machine Learning Research  Volume 8, Issue
12/1/2007
2736 pages
ISSN:1532-4435
EISSN:1533-7928
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JMLR.org

Publication History

Published: 01 December 2007
Published in JMLR Volume 8

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Cited By

View all
  • (2022)Dynamic Gaussian Embedding of AuthorsProceedings of the ACM Web Conference 202210.1145/3485447.3512084(2109-2119)Online publication date: 25-Apr-2022
  • (2022)Efficient Processing of Sparse Tensor Decomposition via Unified Abstraction and PE-Interactive ArchitectureIEEE Transactions on Computers10.1109/TC.2020.304661771:2(266-281)Online publication date: 1-Feb-2022
  • (2022)A Krylov-Schur-like method for computing the best rank-(r1,r2,r3) approximation of large and sparse tensorsNumerical Algorithms10.1007/s11075-022-01303-091:3(1315-1347)Online publication date: 1-Nov-2022
  • (2021)Beyond the signsProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541928(21782-21794)Online publication date: 6-Dec-2021
  • (2021)Measuring International Online Human Values with Word EmbeddingsACM Transactions on the Web10.1145/350130616:2(1-38)Online publication date: 22-Dec-2021
  • (2021)Density Guarantee on Finding Multiple Subgraphs and SubtensorsACM Transactions on Knowledge Discovery from Data10.1145/344666815:5(1-32)Online publication date: 10-May-2021
  • (2020)Ultrahyperbolic representation learningProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3495865(1668-1678)Online publication date: 6-Dec-2020
  • (2020)Unsupervised representation learning with Minimax distance measuresMachine Language10.1007/s10994-020-05886-4109:11(2063-2097)Online publication date: 1-Nov-2020
  • (2020)Lexifield: a system for the automatic building of lexicons by semantic expansion of short word listsKnowledge and Information Systems10.1007/s10115-020-01451-662:8(3181-3201)Online publication date: 1-Aug-2020
  • (2019)Focusing Attention Network for Answer RankingThe World Wide Web Conference10.1145/3308558.3313518(3384-3390)Online publication date: 13-May-2019
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