Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract    new methods for fast computation of the chordal kernels are proposed. Two versions of the chordal kernels for tensor data are discussed. These are based on different projectors of the flattened matrices obtained from the input tensors. A direct transformation of multidimensional objects into the kernel feature space leads to better data separation which can result with a higher classification accuracy. Our approach to more efficient computation of the chordal distances beten tensors is based on an analysis of the tensor projectors which exhibit different properties. Thanks to this an efficient eigen-decomposition becomes possible which is done with a version of the fixed-point algorithm. Experimental results show that our method allows significant speed-up factors, depending mostly on tensor dimensions.
['Bogusław Cyganek', 'Michal Wozniak']
I am writing a research paper on a idea Efficient Computation of the Tensor Chordal Kernels.
[{"from": "human", "value": "I am writing a research paper on a idea Efficient Computation of the Tensor Chordal Kernels."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract    new methods for fast computation of the chordal kernels are proposed. Two versions of the chordal kernels for tensor data are discussed. These are based on different projectors of the flattened matrices obtained from the input tensors. A direct transformation of multidimensional objects into the kernel feature space leads to better data separation which can result with a higher classification accuracy. Our approach to more efficient computation of the chordal distances beten tensors is based on an analysis of the tensor projectors which exhibit different properties. Thanks to this an efficient eigen-decomposition becomes possible which is done with a version of the fixed-point algorithm. Experimental results show that our method allows significant speed-up factors, depending mostly on tensor dimensions."}]