Manifold Alignment with Label Information
Andres F. Duque, Myriam Lizotte, Guy Wolf, Kevin R. Moon

TL;DR
This paper introduces MALI, a novel manifold alignment method that leverages label information to align two distinct data domains, improving cross-domain analysis and transfer learning.
Contribution
MALI combines diffusion-based manifold learning with label guidance, bridging semi-supervised and unsupervised alignment for better domain correspondence.
Findings
MALI outperforms existing methods on multiple datasets.
It effectively recovers sample pairings across domains.
Enables improved domain adaptation and transfer learning.
Abstract
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI can be considered as belonging to a middle ground between the more commonly addressed semi-supervised manifold alignment problem with some known correspondences between the two domains, and the purely unsupervised case, where no known correspondences are provided. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. By aligning…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning
MethodsDiffusion
