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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Correntropy induced metric based graph regularized non-negative matrix factorization
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Correntropy induced metric based graph regularized non-negative matrix factorization

机译：基于熵诱导度量的图正则化非负矩阵分解

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摘要

Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to overcome the aforementioned problems. CGNMF maximizes the correntropy between data matrix and its reconstruction to filter out the noises of large magnitudes, and expects the coefficients to preserve the intrinsic geometric structure of data. We also proposed a modified version of our CGNMF which construct the adjacent graph by using sparse representation to enhance its reliability. Experimental results on popular image datasets confirm the effectiveness of CGNMF.
机译：非负矩阵分解（NMF）是一种有效的降维方法，在许多模式识别和计算机视觉任务中起着重要作用。但是，传统的NMF方法不稳健，因为目标函数对异常值敏感，并且不考虑数据集中的几何结构。在本文中，我们提出了一个熵图正则化NMF（CGNMF）来克服上述问题。 CGNMF最大化数据矩阵及其重构之间的熵，以滤除大幅度的噪声，并期望这些系数保留数据的固有几何结构。我们还提出了CGNMF的修改版本，该版本通过使用稀疏表示来构造相邻图以增强其可靠性。在流行图像数据集上的实验结果证实了CGNMF的有效性。

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