IEEE Trans Neural Netw Learn Syst - MTC: A Fast and Robust Graph-Based Transductive Learning Method.

Tópicos

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Resumo

Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this paper, we propose an efficient and robust graph-based transductive classification method, called minimum tree cut (MTC), which is suitable for large-scale data. Motivated from the sparse representation of graph, we approximate a graph by a spanning tree. Exploiting the simple structure, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. This significantly improves graph-based methods, which typically have a polynomial time complexity. Moreover, we theoretically and empirically show that the performance of MTC is robust to the graph construction, overcoming another big problem of traditional graph-based methods. Extensive experiments on public data sets and applications on web-spam detection and interactive image segmentation demonstrate our method's advantages in aspect of accuracy, speed, and robustness.

Resumo Limpo

despit great success graphbas transduct learn method serious problem scalabl robust paper propos effici robust graphbas transduct classif method call minimum tree cut mtc suitabl largescal data motiv spars represent graph approxim graph span tree exploit simpl structur develop lineartim algorithm label tree cut size tree minim signific improv graphbas method typic polynomi time complex moreov theoret empir show perform mtc robust graph construct overcom anoth big problem tradit graphbas method extens experi public data set applic webspam detect interact imag segment demonstr method advantag aspect accuraci speed robust

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