title: Data fusion and multicue data matching by diffusion maps.
abstract: Data fusion and multicue data matching are fundamental tasks of high-dimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is three-fold: First, we present the Laplace-Beltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a refinement of the Nyström extension algorithm called "geometric harmonics." We also explain how to use this tool for data assimilation. Finally, we introduce a multicue data matching scheme based on nonlinear spectral graphs alignment. The effectiveness of the presented schemes is validated by applying it to the problems of lipreading and image sequence alignment.
authors.affiliation: Google Inc, Mountain View, CA 94043, USA. stephane.lafon@gmail.com
authors.forename: Stéphane
authors.initials: S
authors.lastname: Lafon
authors.forename: Yosi
authors.initials: Y
authors.lastname: Keller
authors.forename: Ronald R
authors.initials: RR
authors.lastname: Coifman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016208:Databases, Factual; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique