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Data fusion and multicue data matching by diffusion maps
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| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 57 - 5 self |
BibTeX
@ARTICLE{Lafon_datafusion,
author = {Stéphane Lafon and Yosi Keller and Ronald R. Coifman},
title = {Data fusion and multicue data matching by diffusion maps},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {},
pages = {2006}
}
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Abstract
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. Index Terms—Pattern matching, graph theory, graph algorithms, Markov processes, machine learning, data mining, image databases. Ç 1
Keyphrases
multicue data matching diffusion map data fusion density invariant embeddings nonlinear spectral graph alignment different source diffusion framework geometric harmonic data mining image sequence alignment image database index term pattern matching graph theory laplace-beltrami approach graph algorithm fundamental task high-dimensional data analysis abstract data fusion data assimilation nystr extension algorithm machine learning multicue data
