However, PCA is an unsupervised dimensionality reduction method, which disregards the discriminative information attached to the semantic component. One of the most typical methods is principal component analysis (PCA), which uses an orthogonal transformation to reduce the dimensionality while preserving unique information (principal component) of the data. In recent years, subspace learning (SL) has made great contributions in the field of machine learning and has a wide application in computer vision, data mining, and so on. Therefore, we mainly focus on cross-view subspace learning to deal with the distribution discrepancy problems in this paper. It generates that the different views become the major factor affecting recognition. This discrepancy means that data variance in the view space is larger than data variance in the class space. However, some researches have indicated that the adaptive ability of traditional machine learning drops sharply, when the learned images have large distribution discrepancy, such as cross-view data. Among them, machine learning can effectively deal with image recognition problems. Under the modern technique background, there are many artificial intelligence methods inspired by nature, such as machine learning, reinforcement learning, and artificial immune recognition. Extensive cross-view classification experiments on several public datasets prove that our proposed method is more effective than other existing feature learning methods. Finally, the global discriminative constraint on distribution center in each view is designed for further alignment improvement. Secondly, a local alignment is constructed with two designed graphs to guide the subspace decomposition in a pairwise way. Firstly, the proposed method utilizes a separable low-rank self-representation model to disentangle the class and view structure layers, respectively. To address this problem, in this paper, we develop a novel cross-view discriminative feature subspace learning method inspired by layered visual perception from human. Nevertheless, the distribution discrepancy between cross-views leads to the fact that instances of the different views from same class are farther than those within the same view but from different classes. Recently, cross-view feature learning has been a hot topic in machine learning due to the wide applications of multiview data.

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