ANALYSES OF BASICS SPARSE CODING
The idea of a method of sparse data representations by matrices with a large number of zero elements is considered. The basic principles of the sparse coding method based on the minimization of the objective function taking into account regularization are presented. The problem of missing values recovery and the application of sparse coding to missing values recovery are discussed. The connection of sparse coding with the Lasso regression analysis method, which allows obtaining a sparse representation, is shown. The prospects for further research, including software implementation of sparse coding in Python, are described.