Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed. First, mapping the data into higher space with kernel principal component analysis to make the data linearly separable. Then building a two-layer KPCANet to obtain the principal components of image. Finally, classifying the principal components with linearly classifier. Experimental results show that the proposed KPCANet is effective in face recognition, object recognition and hand-writing digits recognition, it also outperforms principal component analysis network (PCANet) generally as ll. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
['Dan Wu', 'Jiasong Wu', 'Rui Zeng', 'Longyu Jiang', 'Lotfi Senhadji', 'Huazhong Shu']
I am writing a research paper on a idea Kernel principal component analysis network for image classification
[{"from": "human", "value": "I am writing a research paper on a idea Kernel principal component analysis network for 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: In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed. First, mapping the data into higher space with kernel principal component analysis to make the data linearly separable. Then building a two-layer KPCANet to obtain the principal components of image. Finally, classifying the principal components with linearly classifier. Experimental results show that the proposed KPCANet is effective in face recognition, object recognition and hand-writing digits recognition, it also outperforms principal component analysis network (PCANet) generally as ll. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation."}]