Proposed a compact yet discriminative feature dedicated to traffic sign recognition problem. Introduced two sparse analytical non-linear classifiers for joint feature selection and classification. The performance of two sparse classifiers is no longer sensitive to the polynomial order and less prone to overfitting. Proposed sparse learners reduce the storage complexity and testing time. With smaller feature size the proposed model can get higher classification accuracy. Verified the model reliability using extensive experiments on several data sets.