| Recently, image classification has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. Based on the success of low-rank matrix recovery which has been applied to statistical learning, computer vision and signal processing, this paper presents a novel low-rank matrix recovery algorithm with discriminate regularization. Standard low-rank matrix recovery algorithm decomposes the original dataset into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed method executes low-rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information is available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers, and provide additional discriminating ability to the standard low-rank models for improved performance. The representative bases learned by the proposed method are encouraged to be structural coherence within the same class, and as independent as possible between classes. Numerical simulations on face recognition tasks demonstrate that the proposed algorithm is competitive with the state-of-the-art alternatives.This paper main focuses on the data representation and classification based on the low-rank matrix recovery. This paper contributions are as follows:(1)This paper introduces the background of data representation and classification, sparse theory,compressed sensing and face recognition.(2)This paper introduces traditional algorithms of data representation and classification, then introduce the most important knowledge point in this paper that is low-rank matrix recovery. Which demonstrates the efficiency in face recognition and Image processing etc.(3)Motivated by the Fisher criterion, the proposed method executes low-rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information is available. This paper gives additional discriminating ability to the standard low-rank models for improved performance.(4)This paper gives the practical application of low-rank matrix recovery with discriminant regularization. That is face recognition system. The experiments on some data sets show that when applying our proposed method on face recognition system the recognition rate could be enhanced. |
