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Updated: Mar 27, 2026
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Cross-validation of matching correlation analysis by resampling matching weights.
1Division of Mathematical Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, Japan.
Summary
A new cross-validation method for matching correlation analysis (MCA) accurately estimates matching error by resampling matching weights. This approach is crucial for dimensionality reduction and applicable to cross-domain data.
Area of Science:
- Machine Learning
- Data Science
- Statistical Analysis
Background:
- Matching weight quantifies data vector association strength.
- Dimensionality reduction often involves linear transformations.
- Existing methods like canonical correlation analysis have limitations.
Purpose of the Study:
- To introduce a novel cross-validation technique for matching correlation analysis (MCA).
- To develop a method for estimating matching error with resampled matching weights.
- To extend MCA for cross-domain data with varying dimensions.
Main Methods:
- Defined matching error as a weighted sum of squared distances.
- Utilized spectral graph embedding for optimal linear transformation (MCA).
- Developed and analyzed a cross-validation scheme by resampling matching weights.
Main Results:
- Asymptotic theory confirms rescaled cross-validation provides an unbiased estimate of matching error.
- Demonstrated the inapplicability of data vector resampling for this problem.
- Introduced cross-domain matching correlation analysis (CDMCA) for multi-domain data.
Conclusions:
- The proposed cross-validation method is effective for MCA, particularly with sampled matching weights.
- CDMCA offers a flexible approach for analyzing data from multiple domains.
- MCA and CDMCA show connections to neural network associative memory models.
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