Joint Learning of Spectral Clustering and Low-Rank Representation Based on Precise Segmentation Cost for Cancer Subtype Identification

Tumor samples clustering based on subspace segmentation is an effective method to discover cancer subtypes. Accurate and reliable identifications of cancer subtypes are crucial for understanding cancer pathogenesis as well as clinical diagnosis and treatment. Joint learning-based subspace clustering methods utilize the high correlation between data affinity and data segmentation for better clustering results. However, existing joint learning-based methods only provide an approximation of the segmentation cost in the joint optimization term, which could lead to sub-optimal results. To address this problem, we propose an algorithm named joint learning of spectral clustering and low-rank representation based on precise segmentation cost (JLSLPS) for cancer subtype identification. In our method, we impose non-negative and symmetric constraints on the low-rank representation matrix so that the representation coefficient can be equivalent to data affinity for the precise representation of segmentation cost. Therefore, the spectral clustering objective can be represented precisely to guide the learning of data affinity and segmentation more effectively. Finally, we solve the optimization problem of JLSLPS by using the linearized alternating direction method with adaptive penalty. We run JLSLPS on 8 cancer gene expression datasets and used 9 state-of-the-art clustering methods for comparison. Experimental results show that our method can increase ACC by 1.00-7.95% and NMI by 2.7-12.22% compared with the other methods, which proves the superiority of the proposed method.