Issue No. 11 - November (2006 vol. 28)
S. Lafon , Google Inc., Mountain View, CA
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 Nystrom 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
Pixel, Geometry, Data analysis, Embedded computing, Data assimilation, Image sequences, Graph theory, Machine learning algorithms, Markov processes, Machine learning
S. Lafon, Y. Keller and R. Coifman, "Data Fusion and Multicue Data Matching by Diffusion Maps," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. 11, pp. 1784-1797, 2009.