Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  review a neural implementation of canonical correlation analysis and show, using ideas suggested by ridge regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity.  develop a second model which not only performs as ll on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing partial least squares regression (at one extreme) to canonical correlation analysis (at the other) and every intermediate operation beten the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, the algorithm acts on such data as a smoother in that the resulting ight vectors are much smoother and more interpretable than the ights without the robustification term.
['Zhenkun Gou', 'Colin Fyfe']
I am writing a research paper on a idea A robust canonical correlation neural network
[{"from": "human", "value": "I am writing a research paper on a idea A robust canonical correlation neural network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  review a neural implementation of canonical correlation analysis and show, using ideas suggested by ridge regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity.  develop a second model which not only performs as ll on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing partial least squares regression (at one extreme) to canonical correlation analysis (at the other) and every intermediate operation beten the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, the algorithm acts on such data as a smoother in that the resulting ight vectors are much smoother and more interpretable than the ights without the robustification term."}]