Manifold Alignment Determination: finding correspondences across different data views

[edit]

Andreas Damianou, Amazon Research Cambridge
Neil D. Lawrence, Amazon Research Cambridge and University of Sheffield
Carl Henrik Ek, University of Bristol

Related Material

Abstract

We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned examples from which it is capable to recover a global alignment through a probabilistic model. The strong, yet flexible regularization provided by the generative model is sufficient to align the views. We provide experiments on both synthetic and real data to highlight the benefit of the proposed approach.


@TechReport{manifold-alignment-determination,
  title = 	 {Manifold Alignment Determination: finding correspondences across different data views},
  author = 	 {Andreas Damianou and Neil D. Lawrence and Carl Henrik Ek},
  year = 	 {2017},
  institution = 	 {},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2017-01-12-manifold-alignment-determination.md},
  url =  	 {http://inverseprobability.com/publications/manifold-alignment-determination.html},
  abstract = 	 {We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned examples from which it is capable to recover a global alignment through a probabilistic model. The strong, yet flexible regularization provided by the generative model is sufficient to align the views. We provide experiments on both synthetic and real data to highlight the benefit of the proposed approach.},
  key = 	 {Damianou:mad16},
  linkpdf = 	 {https://arxiv.org/abs/1701.03449.pdf},
  OPTgroup = 	 {}
 

}
%T Manifold Alignment Determination: finding correspondences across different data views
%A Andreas Damianou and Neil D. Lawrence and Carl Henrik Ek
%B 
%D 
%F manifold-alignment-determination	
%P --
%R 
%U http://inverseprobability.com/publications/manifold-alignment-determination.html
%X We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned examples from which it is capable to recover a global alignment through a probabilistic model. The strong, yet flexible regularization provided by the generative model is sufficient to align the views. We provide experiments on both synthetic and real data to highlight the benefit of the proposed approach.
TY  - CPAPER
TI  - Manifold Alignment Determination: finding correspondences across different data views
AU  - Andreas Damianou
AU  - Neil D. Lawrence
AU  - Carl Henrik Ek
PY  - 2017/01/12
DA  - 2017/01/12	
ID  - manifold-alignment-determination	
SP  - 
EP  - 
L1  - https://arxiv.org/abs/1701.03449.pdf
UR  - http://inverseprobability.com/publications/manifold-alignment-determination.html
AB  - We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned examples from which it is capable to recover a global alignment through a probabilistic model. The strong, yet flexible regularization provided by the generative model is sufficient to align the views. We provide experiments on both synthetic and real data to highlight the benefit of the proposed approach.
ER  -

Damianou, A., Lawrence, N.D. & Ek, C.H.. (2017). Manifold Alignment Determination: finding correspondences across different data views.:-