Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations
IEEE Transactions on Information Theory
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Graph-based semi-supervised learning strategy plays an important role in the semi-supervised learning area. This paper presents a novel label propagation algorithm based on nonnegative sparse representation (NSR) for bioinformatics and biometrics. Firstly, we construct a sparse probability graph (SPG) whose nonnegative weight coefficients are derived by nonnegative sparse representation algorithm. The weights of SPG naturally reveal the clustering relationship of labeled and unlabeled samples; meanwhile automatically select appropriate adjacency structure as compared to traditional semi-supervised learning algorithm. Then the labels of unlabeled samples are propagated until algorithm converges. Extensive experimental results on biometrics, UCI machine learning and TDT2 text datasets demonstrate that label propagation algorithm based on NSR outperforms the standard label propagation algorithm.