Manifold Alignment Determination: finding correspondences across different data views

Andreas Damianou; Neil D. Lawrence; Carl Henrik Ek

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Manifold Alignment Determination: finding correspondences across different data views

Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

, 2017.

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.

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Cite this Paper

BibTeX


@Misc{Damianou:mad16,
  title = 	 {Manifold Alignment Determination: finding correspondences across different data views},
  author = 	 {Damianou, Andreas and Lawrence, Neil D. and Ek, Carl Henrik},
  year = 	 {2017},
  pdf = 	 {https://arxiv.org/abs/1701.03449.pdf},
  url = 	 {/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.}
}

Endnote

%0 Generic
%T Manifold Alignment Determination: finding correspondences across different data views
%A Andreas Damianou
%A Neil D. Lawrence
%A Carl Henrik Ek
%D 2017	
%F Damianou:mad16
%U /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.

RIS


TY  - GEN
TI  - Manifold Alignment Determination: finding correspondences across different data views
AU  - Andreas Damianou
AU  - Neil D. Lawrence
AU  - Carl Henrik Ek
DA  - 2017/01/12	
ID  - Damianou:mad16
L1  - https://arxiv.org/abs/1701.03449.pdf
UR  - /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  -

APA


Damianou, A., Lawrence, N.D. & Ek, C.H.. (2017). Manifold Alignment Determination: finding correspondences across different data views.  Available from /publications/manifold-alignment-determination.html.