Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Fisher’s linear discriminant analysis (LDA) is a widely used technique for pattern classi"cation problems. It employs Fisher’s ratio, ratio of beten-class scatter matrix to within-class scatter matrix to derive a set of feature vectors by which high-dimensional data can be projected onto a low-dimensional feature space in the sense of maximizing class separability.  presents a linear constrained distance-based discriminant analysis (LCDA) that uses a criterion for optimality derived from Fisher’s ratio criterion. It not only maximizes the ratio of inter-distance beten classes to intra-distance within classes but also imposes a constraint that all class centers must be aligned along predetermined directions. When these desired directions are orthogonal, the resulting classi"er turns out to have the same operation form as the classi"er derived by the orthogonal subspace projection (OSP) approach recently developed for hyperspectral image classi"cation. Because of that, LCDA can be vied as a constrained version of OSP. In order to demonstrate its performance in hyperspectral image classi"cation, Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and HYperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
['Qian Du', 'Chein-I Chang']
I am writing a research paper on a idea A linear constrained distance-based discriminant analysis for hyperspectral image classification
[{"from": "human", "value": "I am writing a research paper on a idea A linear constrained distance-based discriminant analysis for hyperspectral image classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Fisher\u2019s linear discriminant analysis (LDA) is a widely used technique for pattern classi\"cation problems. It employs Fisher\u2019s ratio, ratio of beten-class scatter matrix to within-class scatter matrix to derive a set of feature vectors by which high-dimensional data can be projected onto a low-dimensional feature space in the sense of maximizing class separability.  presents a linear constrained distance-based discriminant analysis (LCDA) that uses a criterion for optimality derived from Fisher\u2019s ratio criterion. It not only maximizes the ratio of inter-distance beten classes to intra-distance within classes but also imposes a constraint that all class centers must be aligned along predetermined directions. When these desired directions are orthogonal, the resulting classi\"er turns out to have the same operation form as the classi\"er derived by the orthogonal subspace projection (OSP) approach recently developed for hyperspectral image classi\"cation. Because of that, LCDA can be vied as a constrained version of OSP. In order to demonstrate its performance in hyperspectral image classi\"cation, Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and HYperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved."}]