Approximate spectral clustering with utilized similarity information using geodesic based hybrid distance measures
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Approximate spectral clustering,Geodesic distances,Hybrid similarity measures,Manifold learning,Cluster validity indices
论文评审过程：Received 16 May 2014, Revised 13 October 2014, Accepted 20 October 2014, Available online 24 October 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.023
