Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The current paper presents a framework for linear feature extraction applicable in both unsupervised and supervised data analysis, as ll as in their hybrid - the semi-supervised scenario. New features are extracted in a filter manner with a multi-modal genetic algorithm that optimizes simultaneously several projection indices. Experimental results show that the new algorithm is able to provide a compact and improved representation of the data set. The use of mixed labeled and unlabeled data under this scenario improves considerably the performance of constrained clustering algorithms such as constrained k-Means.
['Mihaela Breaban']
I am writing a research paper on a idea Multiobjective projection pursuit for semisupervised feature extraction
[{"from": "human", "value": "I am writing a research paper on a idea Multiobjective projection pursuit for semisupervised feature extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The current paper presents a framework for linear feature extraction applicable in both unsupervised and supervised data analysis, as ll as in their hybrid - the semi-supervised scenario. New features are extracted in a filter manner with a multi-modal genetic algorithm that optimizes simultaneously several projection indices. Experimental results show that the new algorithm is able to provide a compact and improved representation of the data set. The use of mixed labeled and unlabeled data under this scenario improves considerably the performance of constrained clustering algorithms such as constrained k-Means."}]