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影响因子：6.79

发表刊物：IEEE Transactions on Image Processing (TIP)

关键字：Sparse representation low-rank representation subspace clustering adaptive low-rank graph affinity matrix

摘要：By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.

合写作者：Shengli Xie,Zongze Wu,Yun Zhang,Junbin Gao

第一作者：Ming Yin

论文类型：期刊论文

期号：2018, 27(8)

页面范围：3716-3728

是否译文：否

发表时间：2018-08-01

收录刊物：SCI