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Zhu, B. et al. Image reconstruction by domain-transform manifold learning.
Guided by the manifold learning embedding results, a novel cache size optimization scheme is developed.
This paper uses manifold learning to address structure in the acoustic space.
You don't ask this map be linear anymore pretty quickly you get into ideas related to manifold learning.
Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace.
To avoid such deficiencies, a manifold learning technique named maximum variance unfolding (MVU) is considered as an alternative.
The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment.
In this paper, a new supervised manifold learning algorithm (S-LapEig) for feature extraction is proposed first.
Nonlinear dimensionality reduction of data lying on multi-cluster manifolds is a crucial issue in manifold learning research.
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In this paper, we propose a novel supervised manifold learning approach, supervised locality discriminant manifold learning (SLDML), for head pose estimation.
These methods are derived from the aspect of the manifold learning and provide good results.
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