EXTRACT NESSENTIAL FACTORS FROM HIGH DIMENSIONAL BIG DATA
The growth of big data presents both challenges and opportunities in innovative research. On one hand, big data with a large amount of information makes it possible to answer scientifically interesting and practically relevant questions. On the other hand, the massive sample size, high dimensionality and complex dependence of big data create computational and statistical challenges that cannot be handled by the conventional analytical methods. It turns out essential factors extracted from data are useful to explain the dynamic structure of data and can provide important information. In this thesis, we proposed three methods aiming to extract fundamental factors from big data. First, we proposed the PIF and SG-ICA to extract factors from high-dimensional data. Secondly, we proposed 3D Image FPCA to extract factors from 3-dimensional functional MRI data. The proposed methods display superior performance compared to conventional methods.
functional PCA, ICA, Penalized Independent factor analysis, Sparse-group independent component analysis, 3D Image FPCA, risk attitude classification
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