Structure-aware preserving projections with applications to medical image clustering

Abstract

The application of dimensionality reduction (DR) in effectively handling high-dimensional data is becoming increasingly prominent. However, mainstream methods in projection learning exhibit a reliance on either global or local structures, resulting in the inability to effectively learn the discriminative projection. To address this issue, we propose a novel unsupervised DR algorithm, called Structure-Aware Preserving Projections (SAPP). Specifically, SAPP exploits a powerful combination of subspace clustering and graph construction to effectively capture the intrinsic structure of the data. It is able to find discriminative projections, which is mainly attributed to two key factors: (1) can capture the local structure of the data by constructing the nearest neighbor graph and introducing the grouping effect of the representation into projection learning; and (2) preserves the global structure and respects the complex relationship between data points by integrating least squares regression into the dimension reduction process. Moreover, SAPP proves to be a versatile framework that is easily extended to semi-supervised scenarios. Extensive experiments on medical image datasets confirm the effectiveness of our proposed method, showcasing superior clustering accuracy compared to state-of-the-art approaches.