融合极端学习机的判别性分析字典学习算法

研究表明,端学习机和判别性字典学习算法在图像分类领域极具有高效和准确的优势。然而,这两种方法也具有各自的缺点,极端学习机对噪声的鲁棒性较差,判别性字典学习算法在分类过程中耗时较长。为统一这种互补性以提高分类性能,文中提出了一种融合极端学习机的判别性分析字典学习模型。该模型利用迭代优化算法学习最优的判别性分析字典和极端学习机分类器。为验证所提算法的有效性,利用人脸数据集进行分类。实验结果表明,与目前较为流行的字典学习算法和极端学习机相比,所提算法在分类过程中具有更好的效果。

Algorithm with Discriminative Analysis Dictionary Learning by Fusing Extreme Learning Machine

Recent researches have shown that the speed advantage of extreme learning machine(ELM)and the accuracy advantage of discriminative dictionary learning(DDL)in the area of image classification.However these two methods have their respective drawbacks,in general,ELM is known to be less robust to noise while DDL is known to be time-consuming.In order to unify such mutual complementarity and further enhance the classification performance,we propose a discriminative analysis dictionary learning fusing extreme learning machine model in this paper.More precisely,the iterative optimization algorithm is used to learn the most optimal discriminative analysis dictionary and extreme learning machine classifier.In order to verify the effect of the proposed algorithm,the face data is used for classification.Experiments demonstrate that our method achieves a better performance than the state-of-the-art dictionary learning algorithms and extreme learning machine in a variety of image classification tasks.