Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Inspired by the fact that the final decision rule is mainly affected by a small subset of the training samples, i.e., Support Vector Machine(SVM) shows that the decision function relies on the few samples that are on or over the margin.  propose a new framework that explicitly strengthen this intuitive fact by adding an $l_1$-norm regularizer.  give different formulations for our framework in different scenarios, and the experiments show that our framework can not only lead to high sparse solutions but also better performance than traditional methods.
['Bin Liu', 'Shuo Chen', 'Mingjie Qian', 'Changshui Zhang']
I am writing a research paper on a idea Sparse Norm-Regularized Reconstructive Coefficients Learning
[{"from": "human", "value": "I am writing a research paper on a idea Sparse Norm-Regularized Reconstructive Coefficients Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Inspired by the fact that the final decision rule is mainly affected by a small subset of the training samples, i.e., Support Vector Machine(SVM) shows that the decision function relies on the few samples that are on or over the margin.  propose a new framework that explicitly strengthen this intuitive fact by adding an $l_1$-norm regularizer.  give different formulations for our framework in different scenarios, and the experiments show that our framework can not only lead to high sparse solutions but also better performance than traditional methods."}]