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Robust image classification by coupling low rank and collaborative representation graphs

机译:通过结合低秩和协作表示图进行鲁棒的图像分类

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A single graph cannot comprehensively describe the true relationship among samples with high dimensionality, especially for the graph-based methods which directly calculate the distances among samples in the Euclidean space. To further improve the performance of the method based on the presentation graph, in this paper we propose a new composite graph, called as low rank representation and collaborative representation (LRRCR) graph. The proposed LRRCR graph can obtain more informative knowledge for robust image classification. The experimental results on three real face image data sets show that the proposed method has better performance when compared to the traditional ones.
机译:单个图不能全面描述高维样本之间的真实关系,尤其是对于直接计算欧几里得空间中样本之间距离的基于图的方法而言。为了进一步提高基于表示图的方法的性能,本文提出了一种新的复合图,称为低秩表示和协作表示(LRRCR)图。提出的LRRCR图可以获取更多的信息知识,以进行鲁棒的图像分类。在三个真实人脸图像数据集上的实验结果表明,与传统方法相比,该方法具有更好的性能。

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