Semi-supervised learning pseudo label assignment method based on clustering fusion

The invention discloses a semi-supervised learning pseudo label assignment method based on clustering fusion, and the method comprises the steps: carrying out the semi-supervised learning of a convolutional neural network with a label-free data set, carrying out the pre-training of the neural network through employing labeled data and label-free data, and extracting data features through employinga trained network; assigning pseudo tags to N pieces of untagged data closest to the tagged data by using a nearest neighbor method; analyzing all the data information by using k-means clustering, and endowing clustered pseudo tags to the data which is not tagged; and continuously training the convolutional neural network by using the obtained label data and pseudo label data to obtain an optimalnetwork for label assignment. The method can be suitable for semi-supervised learning under deep learning in various fields; information of label-free data can be fully mined, and training data withricher content are provided