Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection beten parts, which can provide a more valid notion of similarity. In general hover, optimal assignments yield indefinite functions, which complicates their use in kernel methods.  characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection.  apply these results by developing the isfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original isfeiler-Lehman kernel.
['Nils Kriege', 'Pierre-Louis Giscard', 'Richard C. Wilson']
I am writing a research paper on a idea On Valid Optimal Assignment Kernels and Applications to Graph Classification
[{"from": "human", "value": "I am writing a research paper on a idea On Valid Optimal Assignment Kernels and Applications to Graph Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection beten parts, which can provide a more valid notion of similarity. In general hover, optimal assignments yield indefinite functions, which complicates their use in kernel methods.  characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection.  apply these results by developing the isfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original isfeiler-Lehman kernel."}]