Issue No. 01 - January (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.209
Bingbing Ni , Advanced Digital Sciences Center, Singapore
Shuicheng Yan , National University of Singapore, Singapore
Ashraf A. Kassim , National University of Singapore, Singapore
In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature representation jointly constitute a harmonic system, where the data labels are invariant with respect to the propagation on the similarity graph constructed based on the optimal feature representation. Based on this philosophy, a unified framework of learning by propagability is proposed for the purposes of both classification and regression. Specifically, this framework has three characteristics: 1) the formulation unifies the label propagation and optimal feature representation pursuing, and thus the label propagation process is enhanced by benefiting from the refined similarity graph constructed with the derived optimal feature representation instead of the original representation; 2) it unifies the formulations for supervised and semisupervised learning in both classification and regression tasks; and 3) it can directly deal with the multiclass classification problems. Extensive experiments for the classification task on UCI toy data sets, handwritten digit recognition, face recognition, and microarray recognition as well as for the regression task of human age estimation on the FG-NET aging database, all validate the effectiveness of our proposed learning framework, compared with the state-of-the-art counterparts.
Semi-supervised learning, graph-based learning, feature extraction, and similarity graph.
B. Ni, A. A. Kassim and S. Yan, "Learning a Propagable Graph for Semisupervised Learning: Classification and Regression," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 114-126, 2010.