Text Classification Using Diffusion Kernel on Statistical Manifold
LI Kan1, ZHOU Shi-Bin2, LIU Yu-Shu1
School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081 School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116
Abstract：Dirichlet compound multinomial manifold (DCM manifold) is proposed. DCM manifold with positive sphere manifold is homeomorphic and isometric, so the geodesic distance of positive sphere manifold can be mapped as the geodesic distance of DCM manifold through pullback mapping. Then the distance metric is built on DCM manifold. DCM diffusion kernel function and DCMIDF diffusion kernel function are built on DCM manifold. The performance of the proposed algorithms for text classification are tested on the corpuses of WebKB Top 4 and 20 Newsgroups, and the experimental results show that DCM manifold is more desirable than that of Euclidean space in modeling texts on the corpuses. Compared with polynomial kernel based support vector machine and NGD kernel based support vector machine, the proposed DCM diffusion kernel and DCMIDF diffusion kernel based support vector machine algorithms show better computational accuracy for text classification.
李侃，周世斌，刘玉树. 统计流形扩散核的文本分类方法[J]. 模式识别与人工智能, 2012, 25(2): 339-345.
LI Kan, ZHOU Shi-Bin, LIU Yu-Shu. Text Classification Using Diffusion Kernel on Statistical Manifold. , 2012, 25(2): 339-345.
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