کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
530847 | 869793 | 2012 | 17 صفحه PDF | دانلود رایگان |

Graph structure is vital to graph based semi-supervised learning. However, the problem of constructing a graph that reflects the underlying data distribution has been seldom investigated in semi-supervised learning, especially for high dimensional data. In this paper, we focus on graph construction for semi-supervised learning and propose a novel method called Semi-Supervised Classification based on Random Subspace Dimensionality Reduction, SSC-RSDR in short. Different from traditional methods that perform graph-based dimensionality reduction and classification in the original space, SSC-RSDR performs these tasks in subspaces. More specifically, SSC-RSDR generates several random subspaces of the original space and applies graph-based semi-supervised dimensionality reduction in these random subspaces. It then constructs graphs in these processed random subspaces and trains semi-supervised classifiers on the graphs. Finally, it combines the resulting base classifiers into an ensemble classifier. Experimental results on face recognition tasks demonstrate that SSC-RSDR not only has superior recognition performance with respect to competitive methods, but also is robust against a wide range of values of input parameters.
► Base classifiers in simple random subspaces are diverse but not accurate.
► Dimensionality reduction is applied in random subspaces instead of original space.
► Base classifiers in the processed random subspaces are diverse and accurate.
► The combined classifier is accurate and robust to a wide range of input values.
► Ensemble classifiers based on random subspaces can be enhanced by this technique.
Journal: Pattern Recognition - Volume 45, Issue 3, March 2012, Pages 1119–1135