International Core Journal of Engineering 2020-26 | Page 124

We propose a semi-supervised structured sparse graph data classification method. The main steps are to first regularize the projection column using the L2 norm by the graph embedding method, then use the general manifold regularization method to reduce the complexity based on Semi-supervised learning algorithm for sparse graphs. It is complemented by the emphasis on spatial complexity in traditional theory. The high-dimension face data X and the low-dimension Y are defined with a transforming T. Formula (3) is the optimization function of this method. lack specificity and compactness. The learning-based local descriptor sub-method is better learned through learning, and the coding also makes the feature more compact. III. S EMI - SUPERVISED S TRUCTURED S PARSE G RAPH C LASSIFICATION The shallow representations still have inevitable limitations, and they are not robust to complex nonlinear face appearance changes. In addition, face recognition generally requires the user to fully cooperate to maintain the image data quality at a good level, but this premise is not satisfied in many cases, so that the available training data is few, even under special conditions, there is only one The image corresponds to an ID. The number of data samples is smaller than the data sample dimension called the small sample size problem. PLQ § ¨ < © DUJ PLQ \ 7 \ \ 7 '\  ; 7 7   · ¸ ¹ (3) The regularized risk function in the manifold structure is the sum of the empirical risk and the regularization factor. The method of minimizing the regularized risk function given a kernel k and function H is [13]: PLQ  P P ¦ [ L  \ L  I [ L  O : I L  + (4) Laplacian regularized least squares classification and Laplace SVM methods perform semi-supervised learning well with or without labeling. Gaussian field and harmonic function are another semi-supervised learning method based on Gaussian random field. The same kind of semi-supervised learning algorithm is proposed for local linear reconstruction coefficient. The semi-supervised classification problem can also be applied to the semi-supervised classification problem by establishing a penalty term on the manifold and unifying the localized discriminant and geometric regularized least squares classification. A graph embedding method refers to a graph in which a node is represented in a vector form. This vector is used to describe nodes and use edge weights to describe node similarity relationships. The general approach to face recognition is to map \ \   \   \ P 7 from high- dimensional face data to low-dimensional. Adjacent data in the original space is also considered to be adjacent in the low-dimensional space. When the node is represented in vector form, the class output can be performed by this method. The method is to minimize the formula (1) and deform it into the formula (2), which preserves the data keeps the local spatial structure.   The L2 norm is used to limit the model space, limit the solution space and reduce the solution space to control the model complexity and reduce the structural problem. Semi-supervised learning method used to learn how to learn from data in a scene with/without tags [11]. The goal is to change the learning style to mix data with/without tags. Supervised algorithms such as clustering and other unsupervised algorithms are classified and used for algorithm design. Semi-supervised learning can also be used as a quantitative tool to understand humans' learning of class attributes. Semi-supervised learning in machine learning and data mining is very effective. It uses unlabeled data to further improve the effectiveness of machine learning and data mining when tag data is very little. PLQ ¦ \ L  \ M ˜ Z LM  Further using a graph-based semi-supervised classifier, the hypothesis map is represented by a matrix : of size Q Q , where Z LM is the non-negative weight (1) between LˈM . This knowledge can be expressed as a penalty for the regularization risk function described above. In particular, the magnitude of the similar node weights indicates whether the nodes have a common similar label. The discriminant function is as follows: (2) Where ' ¦ Z LM .The optimal solution of y can be obtained by finding the eigenvalue. PLQ The universal manifold regularization framework can be used to solve a variety of learning problems from unsupervised, semi-supervised to supervised [12]. The general manifold regularization framework can be used to solve a variety of learning problems from unsupervised, semi-supervised to supervised, and a new regularized framework representation can be generated by using kernel Hilbert space in the priors of inherent low-dimensional manifolds. Additional penalty terms for measuring manifold smoothness enhance the ability to represent data through an inherent structure.  P P ¦ \ L  I [ L  O  I L  Where I [ function. ¦ F LM . [ L  [ , F  +  O  I 7 I (5) is square loss IV. E XPERIMENT R ESULTS In order to compare and validate the method presented in this paper, the standard face data set CASIA-FaceV5 was used. Contains photos of 500 people, 5 for each, 2500 photos, and the face image is a 16-bit color BMP file with dimensions 480 and width 640. Here we divide the data into 102