统一框架的增强深度子空间聚类方法

Enhanced deep subspace clustering method with unified framework

深度子空间聚类是一种处理高维数据聚类任务的有效方法。然而,现有的深度子空间聚类方法通常将自表示学习和指标学习作为两个独立的过程,导致在处理具有挑战性的数据时,固定的自表示矩阵会导致次优的聚类结果;另外,自表示矩阵的质量对聚类结果的准确性至关重要。针对上述问题,提出一种统一框架的增强深度子空间聚类方法。首先,通过将特征学习、自表示学习和指标学习集成在一起同时优化所有参数,根据数据的特征动态地学习自表示矩阵,确保准确地捕捉数据特征;其次,为了提高自表示学习的效果,提出类原型伪标签学习,为特征学习和指标学习提供自监督信息,进而促进自表示学习;最后,为了增强嵌入表示的判别能力,引入正交性约束帮助实现自表示属性。实验结果表明,与AASSC(Adaptive Attribute and Structure Subspace Clustering network)相比,所提方法在MNIST、UMIST、COIL20数据集上的聚类准确率分别提升了1.84、0.49、0.34个百分点。可见,所提方法提高了自表示矩阵学习的准确性,聚类效果更好。

Deep subspace clustering is a method that performs well in processing high-dimensional data clustering tasks.However,when dealing with challenging data,current deep subspace clustering methods with fixed self-expressive matrix usually exhibit suboptimal clustering results due to the conventional practice of treating self-expressive learning and indicator learning as two separate and independent processes,and the quality of self-expressive matrix has a crucial impact on the accuracy of clustering results.To solve the above problems,an enhanced deep subspace clustering method with unified framework was proposed.Firstly,by integrating feature learning,self-expressive learning,and indicator learning together to optimize all parameters,the self-expressive matrix was dynamically learned based on the characteristics of the data,ensuring accurate capture of data features.Secondly,to improve the effects of self-representative learning,class prototype pseudo-label learning was proposed to provide self-supervised information for feature learning and indicator learning,thereby promoting self-expressive learning.Finally,to enhance the discriminative ability of embedded representations,orthogonality constraints were introduced to help achieve self-expressive attribute.The experimental results show that compared with AASSC(Adaptive Attribute and Structure Subspace Clustering network),the proposed method improves clustering accuracy by 1.84,0.49 and 0.34 percentage points on the MNIST,UMIST and COIL20 datasets.It can be seen that the proposed method improves the accuracy of self-representative matrix learning,thereby achieving better clustering effects.