Error-correcting factorization
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved i...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
[October 2018]
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| In: |
IEEE transactions on pattern analysis and machine intelligence
Year: 2018, Volume: 40, Issue: 10, Pages: 2388-2401 |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2017.2763146 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1109/TPAMI.2017.2763146
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| Author Notes: | Miguel Ángel Bautista Martin, Oriol Pujol, Fernando de la Torre, and Sergio Escalera |
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| 520 | |a Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method. Our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. | ||
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| 650 | 4 | |a confusable classes | |
| 650 | 4 | |a core problem | |
| 650 | 4 | |a design matrix | |
| 650 | 4 | |a discrete optimization problem | |
| 650 | 4 | |a ECOC | |
| 650 | 4 | |a Electronic mail | |
| 650 | 4 | |a Encoding | |
| 650 | 4 | |a error correcting output codes | |
| 650 | 4 | |a error correction | |
| 650 | 4 | |a error correction codes | |
| 650 | 4 | |a error-correcting factorization method | |
| 650 | 4 | |a Error-correcting output codes | |
| 650 | 4 | |a error-correction capability | |
| 650 | 4 | |a general error-correcting capability | |
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a Market research | |
| 650 | 4 | |a matrix factorization | |
| 650 | 4 | |a multi-class learning | |
| 650 | 4 | |a multiclass classification | |
| 650 | 4 | |a multiclass problem | |
| 650 | 4 | |a optimal code length | |
| 650 | 4 | |a optimisation | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a pair-wise error-correction | |
| 650 | 4 | |a pattern classification | |
| 650 | 4 | |a pattern recognition | |
| 650 | 4 | |a Support vector machines | |
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