Principal component analysis (PCA) is a typical unsupervised dimensionality reduction algorithm, and one of its important weaknesses is that the squared $\ell _{2}$ -norm cannot overcome the influence of outliers. Existing robust PCA methods based on paradigm have the following two drawbacks. First, the objective function of PCA based on the $\ell _{1}$ -norm has no rotational invariance and limited robustness to outliers, and its solution mostly uses a greedy search strategy, which is expensive. Second, the robust PCA based on the $\ell _{2,1}$ -norm and the $\ell _{2,p}$ -norm is essential to learn probability weights for data, which only weakens the influence of outliers on the learning projection matrix and cannot be completely eliminated. Moreover, the ability to detect anomalies is also very poor. To solve these problems, we propose a novel discrete robust principal component analysis (DRPCA). Through self-learning binary weights, the influence of outliers on the projection matrix and data center estimation can be completely eliminated, and anomaly detection can be directly performed. In addition, an alternating iterative optimization algorithm is designed to solve the proposed problem and realize the automatic update of binary weights. Finally, our proposed model is successfully applied to anomaly detection applications, and experimental results demonstrate that the superiority of our proposed method compared with the state-of-the-art methods.