Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   introduce a novel method that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions. Our method can be regarded as a natural extension of the one-class SVM (OCSVM) algorithm that finds multiple parallel separating hyperplanes in a reproducing kernel Hilbert space.  call our method q-OCSVM, as it can be used to estimate q quantiles of a high-dimensional distribution. For this purpose,  introduce a new global convex optimization program that finds all estimated sets at once and show that it can be solved efficiently.  prove the correctness of our method and present empirical results that demonstrate its superiority over existing methods.
['Assaf Glazer', 'Michael Lindenbaum', 'Shaul Markovitch']
I am writing a research paper on a idea q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
[{"from": "human", "value": "I am writing a research paper on a idea q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   introduce a novel method that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions. Our method can be regarded as a natural extension of the one-class SVM (OCSVM) algorithm that finds multiple parallel separating hyperplanes in a reproducing kernel Hilbert space.  call our method q-OCSVM, as it can be used to estimate q quantiles of a high-dimensional distribution. For this purpose,  introduce a new global convex optimization program that finds all estimated sets at once and show that it can be solved efficiently.  prove the correctness of our method and present empirical results that demonstrate its superiority over existing methods."}]