Multiple Manifold Clustering Using Curvature Constrained Path

• Autor(a) principal: Babaeian, Amir
• Data de Publicação: 2015
• Outros Autores: Bayestehtashk, Alireza, Bandarabadi, Mojtaba
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/109281; https://doi.org/10.1371/journal.pone.0137986
• topic: Artificial Intelligence; Computer Simulation; Humans; Pattern Recognition, Automated; Algorithms; Cluster Analysis; Decision Support Techniques; Models, Theoretical

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.