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Matching datasets of multiple modalities has become an important task in dataanalysis. Existing methods often rely on the embedding and transformation ofeach single modality without utilizing any correspondence information, whichoften results in sub-optimal matching performance. In this paper, we propose anonlinear manifold matching algorithm using shortest-path distance and jointneighborhood selection. Specifically, a joint nearest-neighbor graph is builtfor all modalities. Then the shortest-path distance within each modality iscalculated from the joint neighborhood graph, followed by embedding into andmatching in a common low-dimensional Euclidean space. Compared to existingalgorithms, our approach exhibits superior performance for matching disparatedatasets of multiple modalities.

Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection