Efficiency investigation of manifold matching for text document classification

  • Authors:
  • Ming Sun;Carey E. Priebe

  • Affiliations:
  • -;-

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

Manifold matching works to identify embeddings of multiple disparate data spaces into the same low-dimensional space, where joint inference can be pursued. It is an enabling methodology for fusion and inference from multiple and massive disparate data sources. In this paper three methods of manifold matching are considered: PoM, which stands for Multidimensional Scaling (MDS) composed with Procrustes; CCA (Canonical Correlation Analysis) and JOFC (Joint Optimization of Fidelity and Commensurability). We present a comparative efficiency investigation of the three methods for a particular text document classification application.