Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2010.209
Title: Learning a propagable graph for semisupervised learning: Classification and regression
Authors: Ni, B.
Yan, S. 
Kassim, A. 
Keywords: and similarity graph
feature extraction
graph-based learning
Semi-supervised learning
Issue Date: 2012
Citation: Ni, B., Yan, S., Kassim, A. (2012). Learning a propagable graph for semisupervised learning: Classification and regression. IEEE Transactions on Knowledge and Data Engineering 24 (1) : 114-126. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2010.209
Abstract: 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. © 2011 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/56474
ISSN: 10414347
DOI: 10.1109/TKDE.2010.209
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