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Graph matching through entropic manifold alignment

CVPR 2011, 2011
In this paper we cast the problem of graph matching as one of non-rigid manifold alignment. The low dimensional manifolds are from the commute time embedding and are matched though coherent point drift. Although there have been a number of attempts to realise graph matching in this way, in this paper we propose a novel information-theoretic measure of ...
Francisco Escolano   +2 more
openaire   +1 more source

Manifold alignment for classification of multitemporal hyperspectral data

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
For hyperspectral image classification, manifold learning has proven to be useful for feature extraction from high dimensional data sets. In a traditional manifold learning framework, a low dimensional manifold describing spectral characteristics is developed for classification.
Hsiuhan Lexie Yang, Melba M. Crawford
openaire   +1 more source

Incremental Alignment Manifold Learning

Journal of Computer Science and Technology, 2011
Zhi Han   +3 more
openaire   +1 more source

Manifold Alignment

2011
Chang Wang   +2 more
openaire   +2 more sources

Affine Alignment of Manifold

International Journal of Digital Content Technology and its Applications, 2011
Gelan Yang -, Yue Wu -, Xue Xu -
openaire   +1 more source

Manifold aligned density estimation.

2010
With the advent of the information technology, the amount of data we are facing today is growing in both the scale and the dimensionality dramatically. It thus raises new challenges for some traditional machine learning tasks. This thesis is mainly concerned with manifold aligned density estimation problems.
openaire   +1 more source

Manifold alignment using discrete surface Ricci flow

CAAI Transactions on Intelligence Technology, 2016
Wenmin Wang, Qun Jin
exaly  

Semisupervised Manifold Alignment of Multimodal Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing, 2014
Devis Tuia   +2 more
exaly  

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