The implementation of non-negative matrix decomorization and its optimization algorithm in face recognition

With the popularization of mobile devices and the explosion of data in this era, more and more images, voice, text and other information need to use machine learning to improve the processing efficiency. In order to reduce the dimension of original processing, improve the learning rate and reduce the storage cost, researchers have studied a large number of data dimension reduction algorithms, which have made great achievements in the past hundred years, including principal component analysis, linear discriminant analysis, independent component analysis and so on. In the matrix decomposition of images, speech and text, the negative value obtained by decomposition often has no practical significance. Therefore, Lee et al. proposed non-negative matrix decomposition in 1999, adding non-negative constraints in the decomposition process to decompose the base matrix and coefficient matrix. Generally, low-dimensional base matrix is replaced by the original data matrix to learn classification. Since non-negative matrix decomposition does not produce enough sparsity in the actual operation process and its nature is unsupervised learning cannot, some researchers propose non-negative matrix decomposition for adding sparsity and matrix decomposition for adding discriminative information, which can reduce the spatial storage and learning time more effectively. Due to the slow convergence rate, some researchers propose local decomposition and two-dimensional non-negative matrix decomposition, which effectively improve the convergence rate. In order to reduce the influence of each type on the same face portrait, the product of each matrix in the same angle can be used as the type limit item to the target function. With two constraint terms, more multi-angle data. In order to obtain face data in different dimensions, this paper also combines the multi-dimensional technology with Multi-view NMF technology, designs a multi-dimensional multi-view face recognition method, and provides a more detailed implementation method. The experiment shows that compared with other techniques in the same kind, the method in the paper can better understand the multi-dimensional and multi-view data, and greatly improve the accuracy of human face.