Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Principal component analysis (PCA) and kernel principal component analysis (KPCA) are classical feature extraction methods. Hover, PCA and KPCA are unsupervised learning methods which always maximize the overall variance and ignore the information of within-class and beten-class. ,  propose a simple yet effective strategy to improve the performance of PCA and then this strategy is generalized to KPCA. The proposed methods utilize within-class auxiliary training samples, which are constructed through linear interpolation method. These within-class auxiliary training samples are used to train and get the principal components. In contrast with conventional PCA and KPCA, our proposed methods have more discriminant information. Several experiments are respectively conducted on XM2VTS face database, United States Postal Service (USPS) handwritten digits database and three UCI repository of machine learning databases, experimental results illustrate the effectiveness of the proposed method.
['Sugen Chen', 'Xiaojun Wu', 'He-Feng Yin']
I am writing a research paper on a idea KPCA method based on within-class auxiliary training samples and its application to pattern classification
[{"from": "human", "value": "I am writing a research paper on a idea KPCA method based on within-class auxiliary training samples and its application to pattern classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Principal component analysis (PCA) and kernel principal component analysis (KPCA) are classical feature extraction methods. Hover, PCA and KPCA are unsupervised learning methods which always maximize the overall variance and ignore the information of within-class and beten-class. ,  propose a simple yet effective strategy to improve the performance of PCA and then this strategy is generalized to KPCA. The proposed methods utilize within-class auxiliary training samples, which are constructed through linear interpolation method. These within-class auxiliary training samples are used to train and get the principal components. In contrast with conventional PCA and KPCA, our proposed methods have more discriminant information. Several experiments are respectively conducted on XM2VTS face database, United States Postal Service (USPS) handwritten digits database and three UCI repository of machine learning databases, experimental results illustrate the effectiveness of the proposed method."}]