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Updated: Apr 4, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
20.6K
Cross-Domain Matching with Squared-Loss Mutual Information.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 10, 2015
Summary
This study introduces a new unsupervised cross-domain matching (CDM) framework using squared-loss mutual information (SMI). It effectively aligns objects and sequences across different domains, offering a promising alternative to existing methods.
Area of Science:
- Machine Learning
- Computer Vision
- Information Theory
Background:
- Cross-domain matching (CDM) seeks unsupervised correspondences between objects in disparate domains.
- Applications include photo summarization and temporal sequence alignment.
Purpose of the Study:
- To propose an information-theoretic framework for unsupervised cross-domain matching (CDM).
- To address challenges in aligning non-linearly related objects and sequences with varying dimensions.
Main Methods:
- Developed an unsupervised CDM framework utilizing squared-loss mutual information (SMI).
- Enabled objective hyper-parameter optimization via cross-validation.
- Handled non-linearly related data and differing dimensions directly.
Main Results:
- Successfully applied the SMI-based CDM method to diverse real-world problems.
- Demonstrated effectiveness in image matching, voice conversion, and video alignment tasks.
- Showcased competitive performance against state-of-the-art CDM techniques.
Conclusions:
- The proposed information-theoretic CDM framework offers a robust and versatile solution.
- It provides a promising alternative for various cross-domain alignment challenges.
- The method's ability to handle complex data relationships and optimize parameters is a key strength.
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