Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As higher-order datasets become more common, researchers are primarily focused on how to analyze and compress them. Hover, the most common challenge encountered in any type of data, including tensor data, is noise. Furthermore, the methods developed for denoising vector or matrix type datasets cannot be applied directly to higherorder datasets. This motivates the development of denoising methods for tensors. ,  propose the use of a multiscale approach for denoising general higher-order datasets. The proposed approach works by decomposing the higher-order data into subtensors, and then denoises the subtensors by recursively exploiting filtered residuals. The method is validated on both hyperspectral image and brain functional connectivity network data.
['Alp Ozdemir', 'Mark A. Iwen', 'Selin Aviyente']
I am writing a research paper on a idea A multiscale approach for tensor denoising
[{"from": "human", "value": "I am writing a research paper on a idea A multiscale approach for tensor denoising"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As higher-order datasets become more common, researchers are primarily focused on how to analyze and compress them. Hover, the most common challenge encountered in any type of data, including tensor data, is noise. Furthermore, the methods developed for denoising vector or matrix type datasets cannot be applied directly to higherorder datasets. This motivates the development of denoising methods for tensors. ,  propose the use of a multiscale approach for denoising general higher-order datasets. The proposed approach works by decomposing the higher-order data into subtensors, and then denoises the subtensors by recursively exploiting filtered residuals. The method is validated on both hyperspectral image and brain functional connectivity network data."}]