Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

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.

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

“…Ref. [28, 29] proposed an image clustering method combining manifold regularized constraint with Lat-LRR. Similar to the image data, the gene expression profile is also constituted by numerical matrix with high redundancy and heavy noise.…”

Constrained bilinear factorization multi-view subspace clustering

“…For the proposed method, it is difficult to prove its convergence, since more than two subproblems are involved during optimization. Inspired by [40,45,44], the convergence discussion will be presented in the experiments section, and comprehensive results shown in next section illustrate the strong and stable convergence of the proposed algorithm.…”

Determining a Flexible Low-Rank Graph Using Subspace Clustering

“…al. fused Laplacain diagram requirements into the standard LRR model to safeguard the geometric data from single side [14] and twoside [4] viewpoint. When all is said in done, this kind of subspace bunching can be assembled into ghostly grouping based strategies, which have been exhibited to perform extremely well for some applications in PC vision [19].…”

“…The hidden suspicion is that the watched information nearly lie in/close to some lowdimensional subspaces [2]. Profited from a pairwise comparability diagram, information grouping is promptly changed into a chart segment issue [3] [4][5] [6].…”