Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as ll as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data  compare the frequency domain features of each dimension to estimate a rescaling (towards making the data isotropic). Also, the spectrum of the training data is compared with several candidate kernel spectrums. From this comparison the best matching kernel is chosen. In these ways, the training data matches better the GP classification kernel function (and hence the underlying assumed correlation characteristics), resulting in a better GP classification result. Test results on both non image and image data show the efficiency and effectiveness of our approach.
['Hang Zhou', 'David Suter']
I am writing a research paper on a idea Improved building detection by Gaussian processes classification via feature space rescale and spectral kernel selection
[{"from": "human", "value": "I am writing a research paper on a idea Improved building detection by Gaussian processes classification via feature space rescale and spectral kernel selection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as ll as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data  compare the frequency domain features of each dimension to estimate a rescaling (towards making the data isotropic). Also, the spectrum of the training data is compared with several candidate kernel spectrums. From this comparison the best matching kernel is chosen. In these ways, the training data matches better the GP classification kernel function (and hence the underlying assumed correlation characteristics), resulting in a better GP classification result. Test results on both non image and image data show the efficiency and effectiveness of our approach."}]