Discriminant error correcting output codes based on spectral clustering
| Autor: | Aijun Xue, Xiaodan Wang, Lei Lei, Yafei Song |
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| Rok vydání: | 2015 |
| Předmět: |
Computational complexity theory
business.industry Confusion matrix Pattern recognition 02 engineering and technology Class discrimination 01 natural sciences Facial recognition system Spectral clustering Multiclass classification 010104 statistics & probability ComputingMethodologies_PATTERNRECOGNITION Discriminant Discriminative model Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence 0101 mathematics business Mathematics |
| Zdroj: | Pattern Analysis and Applications. 20:653-671 |
| ISSN: | 1433-755X 1433-7541 |
| Popis: | Error correcting output codes (ECOCs) is a powerful framework to solve the multi-class problems. Finding the optimal partitions with maximum class discrimination efficiently is a key point to improve its performance. In this paper, we propose an alternative and efficient approach to obtain the partitions which are discriminative in the class space. The main idea of the proposed method is to transform the partition in the class space into the cut for an undirected graph using spectral clustering. In addition to measuring the class similarity, the confusion matrix with a pre-classifier is used. Our method is compared with the classical ECOC and DECOC over a synthetic dataset, a set of UCI machine learning repository datasets and one face recognition application. The results show that our proposal is able to obtain comparable or even better classification accuracy while reducing the computational complexity in comparison with the state-of-the-art coding methods. |
| Databáze: | OpenAIRE |
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