Learning representations from multiple manifolds
作者：
Highlights：
• A framework to learn joint embedding space from multiple manifold data is presented.
• Intra-manifolds' geometric structure and inter-manifolds' structure are preserved.
• Implicit correspondence between the points across different data sets is estimated.
• Several embedding examples in different data sets are provided.
• Current spectral-embedding approaches are extended to handle multiple manifolds.
摘要
Highlights•A framework to learn joint embedding space from multiple manifold data is presented.•Intra-manifolds' geometric structure and inter-manifolds' structure are preserved.•Implicit correspondence between the points across different data sets is estimated.•Several embedding examples in different data sets are provided.•Current spectral-embedding approaches are extended to handle multiple manifolds.
论文关键词：Manifold learning,Dimensionality reduction,Joint manifold representation,Correspondence
论文评审过程：Received 12 October 2014, Revised 10 July 2015, Accepted 25 August 2015, Available online 5 September 2015, Version of Record 5 November 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.08.024
