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Manifold integration

Manifold integration is a combined concept of manifold learning and data integration, or an extension of manifold learning for multiple measurements… 
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Papers overview

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2018
2018
In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial… 
Highly Cited
2016
Highly Cited
2016
Industrial automation systems continuously get more complex and growing over time. This leads to an ever growing demand for… 
Highly Cited
2016
Highly Cited
2016
Recently, skeleton-based human action recognition has been receiving significant attention from various research communities due… 
2016
2016
We present a novel and efficient technique to extract Lagrangian coherent structures in two‐dimensional time‐dependent vector… 
2014
2014
This Doctor of Philosophy submission comprises a folio of original compositions, including some audio and visual recordings, with… 
2010
2010
Manifold learning has been successfully used for finding dominant factors (low-dimensional manifold) in a high-dimensional data… 
2009
2009
Scholarship in geography has underscored the importance of emotions to our understanding of space and society. However, the… 
2008
2008
Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space… 
1993
1993
A multibody model of hands and arms is presented. Representative points of contact are considered as the end-effectors. The…