Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection
This work addresses the challenge of aligning disparate datasets for data analysis, but it appears incremental as it builds on existing manifold matching techniques.
The paper tackles the problem of matching datasets from multiple modalities by proposing a nonlinear manifold matching algorithm that uses shortest-path distance and joint neighborhood selection, resulting in superior performance compared to existing methods.
Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear manifold matching algorithm using shortest-path distance and joint neighborhood selection. Specifically, a joint nearest-neighbor graph is built for all modalities. Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space. Compared to existing algorithms, our approach exhibits superior performance for matching disparate datasets of multiple modalities.