# Manifold Alignment Determination: finding correspondences across different data views

## TL;DR

MAD is a probabilistic algorithm that learns correspondences across different data views or modalities using minimal aligned examples, effectively recovering global alignments with strong regularization.

## Contribution

The paper introduces MAD, a novel probabilistic method for manifold alignment that requires few aligned examples to learn accurate correspondences across views.

## Key findings

- Effective on synthetic and real data
- Requires only a few aligned examples
- Successfully recovers global alignments

## Abstract

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.