Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures
Geodesic based hybrid similarity measures are proposed for approximate spectral clustering. Neighborhood graph, required for geodesic approach, is effectively constructed by weighted Delaunay triangulation (CONN). The proposed geodesic based hybrid similarities outperform in terms of both accuracies and cluster validity indices. The proposed geodesic based hybrid similarities can be powerful for clustering of large remote sensing images. The proposed similarities are significant especially for quantization based approximate spectral clustering.
approximate spectral clustering hybrid similarity measures cluster validity indices geodesic distances manifold learning