Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Profile data have received substantial attention in the quality control literature. Most of the prior work has focused on the profile monitoring problem of detecting sudden changes in the characteristics of the profiles, relative to an in-control sample set of profiles. In this article,  present an approach for exploratory analysis of a sample of profiles, the purpose of which is to discover the nature of any profile-to-profile variation that is present over the sample. This is especially challenging in big data environments in which the sample consists of a stream of high-dimensional profiles, such as image or point cloud data.  use manifold learning methods to find a low-dimensional representation of the variation, follod by a supervised learning step to map the low-dimensional representation back into the profile space. The mapping can be used for graphical animation and visualization of the nature of the variation, to facilitate root cause diagnosis. Although this mapping is related to a nonline...
['Zhenyu Shi', 'Daniel W. Apley', 'George C. Runger']
I am writing a research paper on a idea Discovering the Nature of Variation in Nonlinear Profile Data
[{"from": "human", "value": "I am writing a research paper on a idea Discovering the Nature of Variation in Nonlinear Profile Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Profile data have received substantial attention in the quality control literature. Most of the prior work has focused on the profile monitoring problem of detecting sudden changes in the characteristics of the profiles, relative to an in-control sample set of profiles. In this article,  present an approach for exploratory analysis of a sample of profiles, the purpose of which is to discover the nature of any profile-to-profile variation that is present over the sample. This is especially challenging in big data environments in which the sample consists of a stream of high-dimensional profiles, such as image or point cloud data.  use manifold learning methods to find a low-dimensional representation of the variation, follod by a supervised learning step to map the low-dimensional representation back into the profile space. The mapping can be used for graphical animation and visualization of the nature of the variation, to facilitate root cause diagnosis. Although this mapping is related to a nonline..."}]