Laplacian Affinity Propagation for Semi-Supervised Object Classification

We solve the semi-supervised multi-class object classification problem by a graph-based learning algorithm, called Laplacian affinity propagation (LAP). The idea is to model and train both labeled and unlabeled data by constructing a local neighborhood affinity graph in a smoothness formulation of L...

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Bibliographic Details
Published in2007 IEEE International Conference on Image Processing Vol. 1; pp. I - 189 - I - 192
Main Authors Yun Fu, Zhu Li, Xi Zhou, Huang, T.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2007
Subjects
Closed-form solution
Clustering algorithms
graph embedding
Information retrieval
Labeling
Laplace equations
Learning systems
local affinity
object classification
Pattern recognition
Semi-supervised learning
Semisupervised learning
spectral clustering
Training data
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Summary:We solve the semi-supervised multi-class object classification problem by a graph-based learning algorithm, called Laplacian affinity propagation (LAP). The idea is to model and train both labeled and unlabeled data by constructing a local neighborhood affinity graph in a smoothness formulation of Laplacian matrix, based on graph mincuts or harmonic energy minimization. The unknown labels for unlabeled data are inferred from an optimized graph embedding procedure subject to the labeled data. Such label-to-unlabel propagation scheme can provide a closed form solution via a learning framework that is flexible for any new design. LAP integrates embedding and classifier together and gives smooth labels with respect to the underlying manifold structure formed by the training data. Object classification experiments on COIL database demonstrate the effectiveness and applicability of such algorithm.
ISBN:9781424414369
1424414369
ISSN:1522-4880
DOI:10.1109/ICIP.2007.4378923