Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Ming YinYi Guo

Abstract

Low-rank representation (LRR) has received considerable attention in subspace segmentation due to its effectiveness in exploring low-dimensional subspace structures embedded in data. To preserve the intrinsic geometrical structure of data, a graph regularizer has been introduced into LRR framework for learning the locality and similarity information within data. However, it is often the case that not only the high-dimensional data reside on a non-linear low-dimensional manifold in the ambient space, but also their features lie on a manifold in feature space. In this paper, we propose a dual graph regularized LRR model (DGLRR) by enforcing preservation of geometric information in both the ambient space and the feature space. The proposed method aims for simultaneously considering the geometric structures of the data manifold and the feature manifold. Furthermore, we extend the DGLRR model to include non-negative constraint, leading to a parts-based representation of data. Experiments are conducted on several image data sets to demonstrate that the proposed method outperforms the state-of-the-art approaches in image clustering.

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Citations

Mar 19, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Guifu LuJian Zou
Apr 27, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Ming YinJunbin Gao
Jun 1, 2016·IEEE Transactions on Cybernetics·Jingjing LiKe Lu
Jul 17, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Luofeng XieGuofu Yin
Jul 12, 2018·IEEE Transactions on Neural Networks and Learning Systems·Yang WangJunbin Gao
May 4, 2017·BioMed Research International·Guoliang Yang, Zhengwei Hu

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