Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the  curse-of-dimensionality . Linear discriminant analysis (LDA) is a ll-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been developed to overcome this problem, and they have been applied successfully in various applications. Hover, there is a lack of a systematic study of the commonalities and differences of these algorithms, as ll as their intrinsic relationships. , a unified framework for generalized LDA is proposed, which elucidates the properties of various algorithms and their relationships. Based on the proposed framework,  show that the matrix computations involved in LDA-based algorithms can be simplified so that the cross-validation procedure for model selection can be performed efficiently.  conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.
['Shuiwang Ji', 'Jieping Ye']
I am writing a research paper on a idea Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
[{"from": "human", "value": "I am writing a research paper on a idea Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the  curse-of-dimensionality . Linear discriminant analysis (LDA) is a ll-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been developed to overcome this problem, and they have been applied successfully in various applications. Hover, there is a lack of a systematic study of the commonalities and differences of these algorithms, as ll as their intrinsic relationships. , a unified framework for generalized LDA is proposed, which elucidates the properties of various algorithms and their relationships. Based on the proposed framework,  show that the matrix computations involved in LDA-based algorithms can be simplified so that the cross-validation procedure for model selection can be performed efficiently.  conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms."}]