关键词: 多源高维数据, 整合分析, 网络结构, 子群识别

Abstract: In the era of big data, the dimensions of collected data are getting increasingly higher, with data sources diversified. Considering multi-source high-dimensional data, this paper proposes a new integrative analysis method using the K-nearest neighbor method to construct a network structure between data sources. It combines Network MCP penalty with separate MCP penalty to not only automatically identify homogeneous datasets and heterogeneous datasets, but also select the important variable sets of each dataset. In this way, our method can simultaneously conduct the model estimation, variable selection and subgroup identification of data sources. Simulation experiments show that the proposed method has a significant advantage in variable selection, parameter estimation and classification prediction accuracy under different settings. Finally, through experiments on real estate lease evaluation datasets which provide latitude and longitude location information for network construction, it is empirically shown that the proposed method can well identify the sub-markets of real estate and has better interpretability.

Key words: Multi-socure High-dimensional Data, Integrative Analysis, Network Structure, Subgroup Identification