Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a variant of the Generalized Matrix Learning Vector Quantization (GMLVQ) for dissimilarity learning on complex-valued data. Complex features can be encountered in various data domains, e.g. Fourier transformed mass spectrometry or image analysis data. Current approaches deal with complex inputs by ignoring the imaginary parts or concatenating real and imaginary parts in one real valued vector. In this contribution  propose a prototype based classification method, which allows to deal with complex-valued data directly. The algorithm is tested on a benchmark data set and for leaf recognition using Zernike moments.  observe that the complex version converges much faster than the original GMLVQ evaluated on the real parts only. The complex version has fer free parameters than using a concatenated vector and is thus computationally more efficient than original GMLVQ
['Kerstin Bunte', 'Frank-Michael Schleif', 'Michael Biehl']
I am writing a research paper on a idea Adaptive Learning for complex-valued data
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive Learning for complex-valued data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a variant of the Generalized Matrix Learning Vector Quantization (GMLVQ) for dissimilarity learning on complex-valued data. Complex features can be encountered in various data domains, e.g. Fourier transformed mass spectrometry or image analysis data. Current approaches deal with complex inputs by ignoring the imaginary parts or concatenating real and imaginary parts in one real valued vector. In this contribution  propose a prototype based classification method, which allows to deal with complex-valued data directly. The algorithm is tested on a benchmark data set and for leaf recognition using Zernike moments.  observe that the complex version converges much faster than the original GMLVQ evaluated on the real parts only. The complex version has fer free parameters than using a concatenated vector and is thus computationally more efficient than original GMLVQ"}]