Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semi-supervised learning aims to utilize unlabeled data in the process of supervised learning. In particular, combining semi-supervised learning with dimension reduction can reduce overfitting caused by small sample size in high dimensional data. By graph representation with similarity edge ights among data samples including both labeled and unlabeled data, statistical and geometricstructures in data are utilized to explore clustering structure of a small number of labeled data samples. Hover, most of semi-supervised dimension reduction methods use the information induced from unlabeled data points to modify only within-class scatter of labeled data, since unlabeled data can not give any information about distance beten classes. ,  propose semi-supervised dimension reduction which reinforcebeten-class distance by using a penalty graph and minimize within-class scatter by using a similarity graph.  apply our approach to extend linear dimension reduction methods such as Linear discriminant analysis (LDA) and Maximum margin criterion (MMC) and demonstrate that modifying beten-class distance as ll can make great impacts on classification performance.
['Gaksoo Lim', 'Cheong Hee Park']
I am writing a research paper on a idea Semi-supervised Dimension Reduction Using Graph-Based Discriminant Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Semi-supervised Dimension Reduction Using Graph-Based Discriminant Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semi-supervised learning aims to utilize unlabeled data in the process of supervised learning. In particular, combining semi-supervised learning with dimension reduction can reduce overfitting caused by small sample size in high dimensional data. By graph representation with similarity edge ights among data samples including both labeled and unlabeled data, statistical and geometricstructures in data are utilized to explore clustering structure of a small number of labeled data samples. Hover, most of semi-supervised dimension reduction methods use the information induced from unlabeled data points to modify only within-class scatter of labeled data, since unlabeled data can not give any information about distance beten classes. ,  propose semi-supervised dimension reduction which reinforcebeten-class distance by using a penalty graph and minimize within-class scatter by using a similarity graph.  apply our approach to extend linear dimension reduction methods such as Linear discriminant analysis (LDA) and Maximum margin criterion (MMC) and demonstrate that modifying beten-class distance as ll can make great impacts on classification performance."}]