Linear neighborhood propagation and its applications.
In this paper, a novel graph-based transductive classification approach, called Linear Neighborhood Propagation, is proposed. The basic idea is to predict the label of a data point according to its neighbors in a linear way. This method can be cast into a second-order intrinsic Gaussian Markov random field framework. Its result corresponds to a solution to an approximate inhomogeneous biharmonic equation with Dirichlet boundary conditions. Different from existing approaches, our approach provides a novel graph structure construction method by introducing multiple-wise edges instead of pairwise edges, and presents an effective scheme to estimate the weights for such multiple-wise edges. To the best of our knowledge, these two contributions are novel for semi-supervised classification. The experimental results on image segmentation and transductive classification demonstrate the effectiveness and efficiency of the proposed approach.
Wang
Jingdong
J
Internet Media Group, Microsoft Research Asia, Haidian District, Beijing, P.R. China. welleast@gmail.com
Wang
Fei
F
Zhang
Changshui
C
Shen
Helen C
HC
Quan
Long
L
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016014:Linear Models; D010363:Pattern Recognition, Automated / Q000379:methods*