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Data Fusion and Multicue Data Matching by Diffusion Maps

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3 Author(s)
Lafon, S. ; Google Inc., Mountain View, CA ; Keller, Y. ; Coifman, R.R.

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

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 11 )