UNSUPERVISED MULTI-GRAPH PROPAGATION FOR RANKING BASED ON ORDER CHANGE TENDENCY
Graph ranking hat attracted remarkable attention in ranking Held since it exploited the cluster and connectivity assumption. However, Its difficult to combine inhomogencous features into one graph, such as the link relation and content similarity in text retrieval. To address the above problems, a novel multi-graph propagation algorithm for ranking named MGP is proposed, which can be applied to both directed and undirected graphs. It is implemented by constructing multiple graphs in inhomogeneous views and combining results to maximize the agreement of multiple graphs. Compared to existing multi-view learning approaches using labeled data as agreement, MGP introduces order change tendency as agreement substituting for label information, which also ensures the combination being uniform. The theoretical analysis on the convergence of MGP is given. Experimental results on Cora and CiteSeer data sets indicate that MGP can make use of inhomogeneous features sufficiently to enhance the ranking performance.
Graph Ranking Multi-Graph Propagation Order Change Tendency
JIN-LI LIU NAN ZHENG MAO-QIANG XIE YA-LOU HUANG
College of software, Nankai University, Tianjin 300071, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2683-2689
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）