Manifold Alignment Determination: finding correspondences across different data views
This addresses the challenge of data integration for researchers in multi-view learning, though it appears incremental as it builds on existing alignment methods.
The paper tackles the problem of learning correspondences across multiple data views or modalities with only a few aligned examples, achieving global alignment through a probabilistic model.
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities. The approach is capable of learning correspondences between views as well as correspondences between individual data-points. The proposed method requires only a few aligned examples from which it is capable to recover a global alignment through a probabilistic model. The strong, yet flexible regularization provided by the generative model is sufficient to align the views. We provide experiments on both synthetic and real data to highlight the benefit of the proposed approach.