Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Entire regularization paths for graph data
Proceedings of the 24th international conference on Machine learning
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure feature selection for graph classification
Proceedings of the 17th ACM conference on Information and knowledge management
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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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. In this paper we consider a supervised learning problem where the features of the data set have intrinsic complexity, and we further assume that the feature intrinsic complexity may be measured by a kernel function. We hypothesize that by regularizing model parameters using the information of feature complexity, we can construct simple yet high quality model that captures the intrinsic structure of the data. Towards the end of testing this hypothesis, we focus on a regression task and have designed an algorithm that incorporate the feature complexity in the learning process, using a kernel matrix weighted L2 norm for regularization, to obtain improved regression performance over conventional learning methods that does not consider the additional information of the feature. We have tested our algorithm using 5 different real-world data sets and have demonstrate the effectiveness of our method.