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Learning representations from multiple manifolds
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DOI：10.1016/j.patcog.2015.08.024.png)
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摘要
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The problem we address in this paper is how to learn joint representation from data lying on multiple manifolds. We are given multiple data sets, and there is an underlying common manifold among the different data sets. Each data set is considered to be an instance of this common manifold. The goal is to achieve an embedding of all the points on all the manifolds in a way that preserves the local structure of each manifold and that, at the same time, collapses all the different manifolds into one manifold in the embedding space while preserving the implicit correspondences between the points across different data sets. We propose a framework to learn embedding of such data, which can preserve the intra-manifolds' local geometric structure and the inter-manifolds' correspondence structure. The proposed solution works as extensions to current state-of-the-art spectral-embedding approaches to handling multiple manifolds. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Manifold learning
Dimensionality reduction
Joint manifold representation
Correspondence
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7.6
论文数:
1.3W
被引数:
4.5W
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Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions基于拓扑保持多个半监督假设的半监督度量学习.png)
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