research-article

Manifold alignment using Procrustes analysis

Authors:

Chang Wang,

Sridhar MahadevanAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 1120 - 1127

https://doi.org/10.1145/1390156.1390297

Published: 05 July 2008 Publication History

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Abstract

In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semi-supervised alignment" in that it results in a mapping that is defined everywhere - when used with a suitable dimensionality reduction method - rather than just on the training data points. We describe and evaluate our approach both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our method including cross-lingual information retrieval and transfer learning in Markov decision processes are presented.

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Maiorca VMoschella LNorelli AFumero MLocatello FRodolà EOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Latent space translation via semantic alignmentProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668540(55394-55414)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668540
Yampolsky TTalmon RLindenbaum O(2023)Domain and Modality Adaptation Using Multi-Kernel Matching2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10290039(1285-1289)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10290039
Zhang PZhao NLiao J(2023)Text-Guided Vector Graphics CustomizationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618232(1-11)Online publication date: 10-Dec-2023
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Index Terms

Manifold alignment using Procrustes analysis
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Theory of computation
  1. Computational complexity and cryptography
    1. Problems, reductions and completeness

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Published In

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 05 July 2008

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Microsoft Research
Intel
IBM

ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Maiorca VMoschella LNorelli AFumero MLocatello FRodolà EOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Latent space translation via semantic alignmentProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668540(55394-55414)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668540
Yampolsky TTalmon RLindenbaum O(2023)Domain and Modality Adaptation Using Multi-Kernel Matching2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10290039(1285-1289)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10290039
Zhang PZhao NLiao J(2023)Text-Guided Vector Graphics CustomizationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618232(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618232
Duque AMorin SWolf GMoon K(2023)Geometry Regularized AutoencodersIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322210445:6(7381-7394)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TPAMI.2022.3222104
Duque Correa ALizotte MWolf GMoon K(2023)Manifold Alignment with Label Information2023 International Conference on Sampling Theory and Applications (SampTA)10.1109/SampTA59647.2023.10301369(1-6)Online publication date: 10-Jul-2023
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Semi-supervised manifold alignment with few correspondences

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Semi-supervised manifold alignment with few correspondences

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