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Hongliang's research focues on feature selection techniques for data sets with low sample size but high dimensionality in which the features have a structured relationship, such as a chain, a tree and a general graph. The data sets are diverse including both vectorial data such as Miroarray and semi-structured data such as graphs. The goal of his research is to build more accurate and interpretable regression or classification models. He is also interested in sparse learning, multi-task learning with structured input and output.
Boosting with Structure Information in the Functional Space: an Application to Graph Classification
as author at Research Sessions,