Background: Semi-supervised learning in the machine learning community has received
widespread attention. Semi-supervised learning can use a small number of tagged samples and a
large number of untagged samples for efficient learning.
Method: In 2014, Kim proposed a new semi-supervised learning method: the minimax label propagation
(MMLP) method. This method reduces time complexity to O (n), with a smaller computation
cost and stronger classification ability than traditional methods. However, classification results
are not accurate in large-scale image classifications. Thus, in this paper, we propose a semisupervised
image classification method, which is an MMLP-based algorithm. The main idea is
threefold: (1) Improving connectivity of image pixels by pixel sampling to reduce the image size,
at the same time, reduce the diversity of image characteristics; (2) Using a recall feature to improve
the MMLP algorithm; (3) through classification mapping, gaining the classification of the original
data from the classification of the data reduction.
Result: In the end, our algorithm also gains a minimax path from untagged samples to tagged samples.
The experimental results proved that this algorithm is applicable to semi-supervised learning
on small-size and that it can also gain better classification results for large-size image at the same
Conclusion: In our paper, considering the connectivity of the neighboring matrix and the diversity
of the characteristics, we used meanshift clustering algorithm,next we will use fuzzy energy clustering[
] on our algorithm. we will study the function of these paths.