Semi-Supervised Collaboration Training Algorithm Based on Codeword Matching and Gravitation Selecting

Traditional collaboration training algorithms, such as Co-training and Tri-training, has some problems: low independence of the base classifier, error accumulation during iteration and low generalization system performance. Accordingly, this paper proposes an import multi-view theory,coding theory and formula for universal gravitation as applied to the collaboration training algorithm preventing both error accumulation and improving generalization performance at the same time. During a hyper-spectral image classification experiment, randomly selecting 5%,10% and 20% samples from data sets as a labled train set, the collaboration training algorithm for codeword matching had a 12.38% and 6.13% higher accuracy matching rate than Co-training and Tri-training respectively. At the same time, it had a respective 0.2 and 0.07 higher Kappa coefficient. In contrast, a collaboration training algorithm for classification based on codeword matching and gravitation selecting had 21.30% and 10.99% higher accuracy than Co-training and Tri-training and 0.26 and 0.17 higher Kappa coefficient. These results demonstrate the validity of the proposed algorithm.