Learning representations from multiple manifolds. (February 2016)

• Record Type: Journal Article
• Authors: Lee, Chan-Su; Elgammal, Ahmed; Torki, Marwan
• Is Part Of: Pattern recognition. Volume 50(2016:Feb.)
• Page Start: 74
• Page End: 87
• Publication Date: 2016-02
• Subjects: Manifold learning -- Dimensionality reduction -- Joint manifold representation -- Correspondence; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.08.024
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Graphical abstract: Abstract : 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.