Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined from graph data are graphs. Such graph features have different number of nodes and edges and usually overlap with each other. In conventional data mining and machine learning applications, the internal structure of features are usually ignored.   consider a supervised learning problem where the features of the data set have intrinsic complexity, and  further assume that the feature intrinsic complexity may be measured by a kernel function.  hypothesize that by regularizing model parameters using the information of feature complexity,  can construct simple yet high quality model that captures the intrinsic structure of the data. Towards the end of testing this hypothesis,  focus on a regression task and have designed an algorithm that incorporate the feature complexity in the learning process, using a kernel matrix ighted L2 norm for regularization, to obtain improved regression performance over conventional learning methods that does not consider the additional information of the feature.  have tested our algorithm using 5 different real-world data sets and have demonstrate the effectiveness of our method.
['Hongliang Fei', 'Jun Huan']
I am writing a research paper on a idea L2 norm regularized feature kernel regression for graph data
[{"from": "human", "value": "I am writing a research paper on a idea L2 norm regularized feature kernel regression for graph data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined from graph data are graphs. Such graph features have different number of nodes and edges and usually overlap with each other. In conventional data mining and machine learning applications, the internal structure of features are usually ignored.   consider a supervised learning problem where the features of the data set have intrinsic complexity, and  further assume that the feature intrinsic complexity may be measured by a kernel function.  hypothesize that by regularizing model parameters using the information of feature complexity,  can construct simple yet high quality model that captures the intrinsic structure of the data. Towards the end of testing this hypothesis,  focus on a regression task and have designed an algorithm that incorporate the feature complexity in the learning process, using a kernel matrix ighted L2 norm for regularization, to obtain improved regression performance over conventional learning methods that does not consider the additional information of the feature.  have tested our algorithm using 5 different real-world data sets and have demonstrate the effectiveness of our method."}]