Data Fusion and Multicue Data Matching by Diffusion Maps

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 LaplaceBeltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a refinement of the Nystro

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Stephane Lafon, Yosi Keller, Ronald R. Coifman
• Keywords: Data Fusion, Density Invariant Embeddings, Multicue Data, PAMI 2006