Direct Spectral Clustering with New Graph Learning for Better Fitting

Traditional spectral clustering methods struggle with scalability and robustness in large datasets due to their reliance on similarity matrices and EigenValue Decomposition. We introduce two innovative models: Rcut-based Coordinate Descent Clustering (R-CDC) and Ncut-based Doubly Stochastic Clustering (N-DSC). These models integrate graph construction and segmentation into a unified process optimized through the coordinate descent method, significantly enhancing clustering efficacy. A novel graph structure enhances robustness against noise and outliers, simplifying the clustering process and improving outcomes across diverse datasets. Our extensive experiments show that these models surpass existing spectral clustering techniques in managing large-scale data and complex structures.