Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dictionary learning (DL), playing a key role in the success of sparse representation, has led to state-of-the-art results in image classification tasks. Among the existing supervised dictionary learning methods, the label of each dictionary atom is predefined and fixed, i.e., each dictionary atom is either associated to all classes or assigned to a single class. ,  propose a structure adaptive dictionary learning (SADL) method to learn the relationship beten dictionary atoms and classes, which is indicated by a binary association matrix and jointly optimized with the dictionary. The binary association matrix can not only represent class-specific dictionary atoms, but also hyper-class dictionary atoms shared by multiple classes. Furthermore, discrimination is explored by introducing Fisher criterion on coding coefficient and reducing beten-class dictionary coherence. The extensive experimental results have shown that the proposed SADL can achieve better performance than previous supervised dictionary learning methods on various classification databases.
['Heyou Chang', 'Meng Yang', 'Jian Yang']
I am writing a research paper on a idea Learning a structure adaptive dictionary for sparse representation based classification
[{"from": "human", "value": "I am writing a research paper on a idea Learning a structure adaptive dictionary for sparse representation based classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dictionary learning (DL), playing a key role in the success of sparse representation, has led to state-of-the-art results in image classification tasks. Among the existing supervised dictionary learning methods, the label of each dictionary atom is predefined and fixed, i.e., each dictionary atom is either associated to all classes or assigned to a single class. ,  propose a structure adaptive dictionary learning (SADL) method to learn the relationship beten dictionary atoms and classes, which is indicated by a binary association matrix and jointly optimized with the dictionary. The binary association matrix can not only represent class-specific dictionary atoms, but also hyper-class dictionary atoms shared by multiple classes. Furthermore, discrimination is explored by introducing Fisher criterion on coding coefficient and reducing beten-class dictionary coherence. The extensive experimental results have shown that the proposed SADL can achieve better performance than previous supervised dictionary learning methods on various classification databases."}]