One-step graph-based multi-view clustering via specific and unified nonnegative embeddings

Abstract

Multi-view clustering techniques, especially spectral clustering methods, are quite popular today in the fields of machine learning and data science owing to the ever-growing diversity in data types and information sources. As the landscape of data continues to evolve, the need for advanced clustering approaches becomes increasingly crucial. In this context, the research in this study addresses the challenges posed by traditional multi-view spectral clustering techniques, offering a novel approach that simultaneously learns nonnegative embedding matrices and spectral embeddings. Moreover, the cluster label matrix, also known as the nonnegative embedding matrix, is split into two different types of matrices: (1) the shared nonnegative embedding matrix, which reflects the common cluster structure, (2) the individual nonnegative embedding matrices, which represent the unique cluster structure of each view. The proposed strategy allows us to effectively deal with noise and outliers in multiple views. The simultaneous optimization of the proposed model is solved efficiently with an alternating minimization scheme. The proposed method exhibits significant improvements, with an average accuracy enhancement of 4% over existing models, as demonstrated through extensive experiments on various real datasets. This highlights the efficacy of the approach in achieving superior clustering results.