Adaptive Weight Structure Representation for Multi-view Subspace Clustering

Shouhang Wang, Yong Wang, Wenge Le
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Abstract

Traditional subspace clustering method based on self-representation has been widely applied in learning similarity matrix. Existing self-representation based methods treat all features equally in the process of learning similarity matrix, which makes redundant features in learning stage may have a certain negative impact on the final representation and even the representation of other non-redundant features. To solve the above problems, this paper proposes Adaptive Weight Low-Rank Representation (AWSLRR) algorithm. AWSLRR uses firstly nested structure to learn more clean and reasonable similarity matrix, then applied weight constraints to the reconstruction corruption which is generated during the reconstruction process. The adaptive weight matrix imposes a small weight coefficient on the larger corruption value by imposing constraints on the corruption term during the self-representation process, and vice versa. Finally, the experimental results on five real datasets validate the competitiveness of proposed algorithm.
多视图子空间聚类的自适应权结构表示
传统的基于自表示的子空间聚类方法在相似性矩阵的学习中得到了广泛的应用。现有的基于自表示的方法在学习相似矩阵的过程中对所有特征都是平等对待的,这使得学习阶段的冗余特征可能会对最终表征甚至其他非冗余特征的表征产生一定的负面影响。为了解决上述问题,本文提出了自适应加权低秩表示(AWSLRR)算法。AWSLRR首先利用嵌套结构学习更干净合理的相似矩阵,然后对重构过程中产生的重构损坏进行权约束。自适应权重矩阵通过在自表示过程中对腐败项施加约束,对较大的腐败值施加较小的权重系数,反之亦然。最后,在5个真实数据集上进行了实验,验证了算法的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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