Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data.  demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data.  investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.
['Stan Z. Li', 'Xiaoguang Lu', 'XinWen Hou', 'Xianhua Peng', 'Qiansheng Cheng']
I am writing a research paper on a idea Learning multiview face subspaces and facial pose estimation using independent component analysis
[{"from": "human", "value": "I am writing a research paper on a idea Learning multiview face subspaces and facial pose estimation using independent component analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data.  demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data.  investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented."}]