A multi-view subspace representation learning approach powered by subspace transformation relationship

Abstract

Traditional multi-view subspace representation learning typically captures the pairwise similarity among data objects but neglects the underlying relationship among subspace representations of different views, resulting in the essential structural information of multi-view data not being fully exploited. In this study, a multi-view representation learning approach is proposed using subspace transformation relationships. First, a subspace transformation relationship is defined to extract the underlying subspace relationship information. The transformation relationship is constrained by a low-rank term to enhance the consistency of the relationships and is used to construct a multi-view subspace representation model. Second, an optimization method is proposed for the multi-view subspace representation model using the ALM-ADM strategy. Third, a novel multi-view subspace representation learning algorithm is presented using the model and optimization method. Consequently, experimental findings on benchmark datasets indicated that the algorithm outperformed the state-of-the-art algorithm, benefitting from extracting subspace transformation relationships to guarantee high-order correlation information from multi-view data.