International Core Journal of Engineering 2020-26 | Page 125

2000 training sets and 500 test sets. We conducted a total of five sets of tests, randomly extracting 1000 data from 2000 training sets as training data, and randomly extracting 100 test sets as test data. The method proposed in this paper is compared with semi-supervised manifold regularization, and the results obtained are shown in Fig. 2. due to environmental influences in the face classification. Experiments on the CASIA-FaceV5 dataset demonstrate that this method has higher face classification accuracy than the existing methods. The next step is to reduce its computational cost. R EFERENCES [1] [2] [3] [4] [5] Figure 2. Average testing accuracy on two methods. It can be seen that the average accuracy of semi- supervised manifold regularization for CASIA-FaceV5 face classification is above 90%. The main reason is that CASIA- FaceV5 is a relatively standard face data set with a small amount of data. Compared with the semi-supervised manifold regularization system, our semi-supervised structured sparse graph data classification test accuracy has almost better test accuracy, both exceeding and reaching 95%. On the other hand, through comparative tests, it can be found that the semi-supervised structured sparse graph data classification has a significant improvement in computational cost compared to semi-supervised manifold regularization. Therefore, it can be considered that the semi-supervised structured sparse graph data classification has good recognition accuracy and needs to be improved in terms of efficiency. [6] [7] [8] [9] [10] [11] V. C ONCULUSION [12] We propose a semi-supervised structured sparse graph data classification method for face classification. The manifold method of retaining the global data structure and the local data structure is adopted, and the L2 norm is used to limit the model space, and the complexity of the model is controlled to alleviate the problem of large data difference [13] 103 Kong X, Yu P S. Semi-supervised feature selection for graph classification[C]濁澳 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 793-802. Kudo T, Maeda E, Matsumoto Y. An application of boosting to graph classification[C]. Advances in neural information processing systems. 2005: 729-736. Riesen K, Bunke H. Graph classification by means of Lipschitz embedding[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(6): 1472-1483. Camps-Valls G, Marsheva T V B, Zhou D. Semi-supervised graph- based hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10): 3044-3054. Zhou D, Hofmann T, Schölkopf B. Semi-supervised learning on directed graphs[C]. Advances in neural information processing systems. 2005: 1633-1640. Luo R, Liu X, Wu Z. Feature Extraction of Hyperspectral Images With Semi-supervised Sparse Graph Learning[C]. 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2018: 1-4. Li J, Rong Y, Cheng H, et al. Semi-Supervised Graph Classification: A Hierarchical Graph Perspective[C]. The World Wide Web Conference. ACM, 2019: 972-982. Li L, Peng Y, Qiu G, et al. A survey of virtual sample generation technology for face recognition[J]. Artificial Intelligence Review, 2018, 50(1): 1-20. NASSIH B, AMINE A, NGADI M, et al. DCT and HOG Feature Sets Combined with BPNN for Efficient Face Classification[J]. Procedia Computer Science, 2019, 148: 116-125. Gou J, Song J, Ou W, et al. Representation-based classification methods with enhanced linear reconstruction measures for face recognition[J]. Computers & Electrical Engineering, 2019, 79: 106451. Zhu X, Goldberg A B. Introduction to semi-supervised learning[J]. Synthesis lectures on artificial intelligence and machine learning, 2009, 3(1): 1-130. Fan M, Gu N, Qiao H, et al. Sparse regularization for semi-supervised classification[J]. Pattern Recognition, 2011, 44(8): 1777-1784. Tsang I W, Kwok J T. Large-scale sparsified manifold regularization[C]. Advances in Neural Information Processing Systems. 2007: 1401-1408.