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Error-Correcting Factorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 17, 2017
Summary
This study introduces Error-Correcting Factorization (ECF) to improve multi-class classification by allocating error correction to confused classes. ECF enhances performance over state-of-the-art methods by enabling targeted error correction in pattern recognition.
Area of Science:
- Pattern Recognition
- Machine Learning
- Multi-class Classification
Background:
- Error Correcting Output Codes (ECOC) is a key multi-class classification technique.
- ECOC decouples problems into independent binary tasks.
- Existing ECOC methods lack analysis of error-correction distribution among classes.
Purpose of the Study:
- Propose Error-Correcting Factorization (ECF) for improved ECOC.
- Enable analysis and allocation of pair-wise error-correction.
- Address limitations in current ECOC error-correction distribution.
Main Methods:
- Introduce a novel 'design matrix' for ECOC error-correction representation.
- Derive optimal ECOC code length using design matrix rank properties.
- Formulate ECF as a discrete optimization problem solved via constrained block coordinate descent.
Main Results:
- ECF enables flexible allocation of error-correction to specific class pairs.
- Targeted allocation of error-correction to confusable classes demonstrated superior performance.
- Experimental results show ECF outperforms state-of-the-art approaches on several databases.
Conclusions:
- ECF provides a novel framework for analyzing and optimizing ECOC.
- The design matrix facilitates targeted error-correction allocation.
- ECF offers significant improvements in multi-class classification, especially for confusable classes.
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