Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition via local patch labelling
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Pattern Recognition Letters
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Label propagation (LP) is used in the framework of semi-supervised learning. In this paper, we propose a novel method of logistic label propagation (LLP). The proposed method employs logistic functions for accurately estimating the label values as the posterior probabilities. In LLP, the label of newly input sample is efficiently estimated by using the optimized coefficients in the logistic function, without such recomputation of all label values as in original LP. In the experiments on classification, the proposed method produced more reliable label values at the high degree of confidence than LP and ordinary logistic regression. In addition, even for a small portion of the labeled samples, the error rates by LLP were lower than those by the logistic regression.