Sparse Representation for Three-Dimensional Number Ball Recognition

We consider the classification problem as a linear regression problem, and find that sparse signal representation offers the key to address this problem. Therefore, a new method, which is based on sparse representation, is proposed for classification. This new method provides insights into two critical issues in classification: sparse representation and classification. For sparse representation, we use the lasso, the elastic net and nonnegative garrote as the initial estimate of a new test sample. In the classification stage, we classify the test sample to the correct class via a simple l²-distance measurement. Finally, we propose an efficient algorithm for computing the whole solution path of this method, and conduct extensive experiments on the number ball recognition. From the experiment results, we conclude that this method achieves high recognition rate.