Learning by Propagability

In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and optimal feature representation can constitute a harmonic system, namely, the data labels are invariant with respect to the propagation on the similarity-graph constructed by the optimal feature representation. Based on this philosophy, a unified formulation for learning by propagability is proposed for both supervised and semisupervised configurations. Specifically, this formulation offers the semi-supervised learning two characteristics: 1) unlike conventional semi-supervised learning algorithms which mostly include at least two parameters, this formulation is parameter-free; and 2) the formulation unifies the label propagation and optimal representation pursuing, and thus the label propagation is enhanced by benefiting from the graph constructed with the derived optimal representation instead of the ori...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loong Fah Cheong
• Keywords: Data Mining, Feature Extraction, ICDM 2008, Optimal Feature Representation, Semi-supervised Learning