Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a linear discriminant analysis method. In this method, every discriminant vector, except for the first one, is worked out by maximizing a Fisher criterion defined in a transformed space which is the null space of the previously obtained discriminant vectors. All of these discriminant vectors are used for dimension reduction.  also propose two algorithms to implement the model. Based on the algorithms,  prove that the discriminant vectors will be orthogonal if the within-class scatter matrix is not singular. The experimental results show that the proposed method is effective and efficient.
['Jinghua Wang', 'Yong Xu', 'David Zhang', 'Jane You']
I am writing a research paper on a idea An efficient method for computing orthogonal discriminant vectors
[{"from": "human", "value": "I am writing a research paper on a idea An efficient method for computing orthogonal discriminant vectors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a linear discriminant analysis method. In this method, every discriminant vector, except for the first one, is worked out by maximizing a Fisher criterion defined in a transformed space which is the null space of the previously obtained discriminant vectors. All of these discriminant vectors are used for dimension reduction.  also propose two algorithms to implement the model. Based on the algorithms,  prove that the discriminant vectors will be orthogonal if the within-class scatter matrix is not singular. The experimental results show that the proposed method is effective and efficient."}]