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Graph-based discriminative concept factorization for data representation

机译：基于图的判别性概念分解用于数据表示

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Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF) have been widely used for different purposes such as feature learning, dimensionality reduction and image clustering in data representation. However, CF is a variant of NMF, which is an unsupervised learning method without making use of the available label information to guide the clustering process. In this paper, we put forward a semi-supervised discriminative concept factorization (SDCF) method, which utilizes the limited label information of the data as a discriminative constraint. This constraint forces the representation of data points within the same class should be very close together or aligned on the same axis in the new representation. Furthermore, in order to utilize the local manifold regularization, we propose a novel semi supervised graph-based discriminative concept factorization (GDCF) method, which incorporates the local manifold regularization and the label information of the data into the CF to improve the performance of CF. GDCF not only encodes the local geometrical structure of the data space by constructing K-nearest graph, but also takes into account the available label information. Thus, the discriminative abilities of data representations are enhanced in the clustering tasks. Experimental results on several databases expose the strength of our proposed SDCF and GDCF methods compared to the state-of-the-art methods. (C) 2016 Elsevier B.V. All rights reserved.
机译：非负矩阵因式分解（NMF）和概念因式分解（CF）已广泛用于不同目的，例如特征学习，降维和数据表示中的图像聚类。但是，CF是NMF的一种变体，它是一种无监督的学习方法，没有利用可用的标签信息来指导聚类过程。在本文中，我们提出了一种半监督的判别概念分解（SDCF）方法，该方法利用数据的有限标签信息作为判别约束。此约束迫使新类中同一类内的数据点的表示应非常靠近或对齐在同一轴上。此外，为了利用局部流形正则化，我们提出了一种新的基于半监督图的判别概念因式分解（GDCF）方法，该方法将局部流形正则化和数据的标签信息合并到CF中以提高CF的性能。 。 GDCF不仅通过构造K最近图来编码数据空间的局部几何结构，而且还考虑了可用的标签信息。因此，在聚类任务中增强了数据表示的判别能力。与最先进的方法相比，在多个数据库上的实验结果揭示了我们提出的SDCF和GDCF方法的优势。 （C）2016 Elsevier B.V.保留所有权利。

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