点击次数：

影响因子：17.73

DOI码：10.1109/TPAMI.2015.2462360

发表刊物：IEEE Transactions on Pattern Analysis and Machine Intelligence

关键字：Low-Rank Representation, Graph, Hyper- Laplacian, Manifold Structure, Laplacian Matrix, Regularization

摘要：Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method.

合写作者：Junbin Gao,Zhouchen Lin

第一作者：Ming Yin

论文类型：期刊论文

期号：2016, 38(3)

页面范围：504-517

是否译文：否

发表时间：2016-03-01

收录刊物：SCI