Abstract：Minimum spanning tree class descriptor (MSTCD) describes the target class with the assumption that all the edges of the graph are basic elements of the classifier, which offers additional virtual training data for a description of sample distribution in highdimensional space. However, this descriptive model has too many branches, which results in the model being more complicated. According to the continuity law of the feature space of similar samples, a one class classification algorithm based on sparse minimum spanning tree covering model is presented. The method firstly constructs sparse k nearestneighbor graph representation for the target class. Then, a recursive graph bipartition algorithm is introduced to find the microcluster. Finally, it builds sparse minimum spanning tree on the graph nodes which are centers of micro cluster. Experimental results show that the presented algorithm performs better than MSTCD and other one class classifiers.
胡正平, 路亮, 许成谦. 基于高维空间稀疏最小生成树自适应覆盖模型的一类分类算法[J]. 模式识别与人工智能, 2011, 24(3): 444-451.
HU Zheng-Ping, LU Liang, XU Cheng-Qian. One Class Classification Algorithm Based on Sparse Minimum Spanning Tree Adaptive Covering Model in HighDimensional Space. , 2011, 24(3): 444-451.
2011, Vol. 24 
