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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Maximum Correntropy Criterion-Based Robust Semisupervised Concept Factorization for Image Representation
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Maximum Correntropy Criterion-Based Robust Semisupervised Concept Factorization for Image Representation

机译：基于校正标准的最大稳健的图像表示的鲁棒半化概念分解

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摘要

Concept factorization (CF) has shown its great advantage for both clustering and data representation and is particularly useful for image representation. Compared with nonnegative matrix factorization (NMF), CF can be applied to data containing negative values. However, the performance of CF method and its extensions will degenerate a lot due to the negative effects of outliers, and CF is an unsupervised method that cannot incorporate label information. In this article, we propose a novel CF method, with a novel model built based on the maximum correntropy criterion (MCC). In order to capture the local geometry information of data, our method integrates the robust adaptive embedding and CF into a unified framework. The label information is utilized in the adaptive learning process. Furthermore, an iterative strategy based on the accelerated block coordinate update is proposed. The convergence property of the proposed method is analyzed to ensure that the algorithm converges to a reliable solution. The experimental results on four real-world image data sets show that the new method can almost always filter out the negative effects of the outliers and outperform several state-of-the-art image representation methods.
机译：概念分解（CF）对聚类和数据表示表示其具有很大优势，并且对图像表示特别有用。与非负矩阵分子（NMF）相比，CF可以应用于包含负值的数据。然而，由于异常值的负面影响，CF方法及其扩展的性能将堕落，并且CF是一种无监督的方法，不能包含标签信息。在本文中，我们提出了一种新颖的CF方法，该方法基于最大正控性标准（MCC）构建了一种新颖的模型。为了捕获数据的局部几何信息，我们的方法将强大的自适应嵌入和CF集成到统一的框架中。标签信息用于自适应学习过程。此外，提出了一种基于加速块坐标更新的迭代策略。分析所提出的方法的收敛性能以确保算法会聚到可靠的解决方案。在四个现实世界图像数据集上的实验结果表明，新方法几乎总是始终会过滤出异常值的负面影响，优于近几种最先进的图像表示方法。

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