Skip to Main Content
 

Global Search Box

 
 
 
 

ETD Abstract Container

Abstract Header

Adaptive Mixture Estimation and Subsampling PCA

Abstract Details

2009, Doctor of Philosophy, Case Western Reserve University, Sciences.
Data mining is important in scientific research, knowledge discovery and decision making. A typical challenge in data mining is that a data set may be too large to be loaded all together, at one time, into computer memory for analyses. Even if it can be loaded all at once for an analysis, too many nuisance features may mask important information in the data. In this dissertation, two new methodologies for analyzing large data are studied. The first methodology is concerned with adaptive estimation of mixture parameters in heterogeneous populations of large-n data. Our adaptive estimation procedures, the partial EM (PEM) and its Bayesian variants (BMAP and BPEM) work well for large or streaming data. They can also handle the situation in which later stage data may contain extra components (a.k.a. "contaminations" or "intrusions") and hence have applications in network traffic analysis and intrusion detection. Furthermore, the partial EM estimate is consistent and efficient. It compares well with a full EM estimate when a full EM procedure is feasible. The second methodology is about subsampling large-p data for selecting important features under the principal component analysis (PCA) framework. Our new method is called subsampling PCA (SPCA). Diagnostic tools for choosing parameter values, such as subsample size and iteration number, in our SPCA procedure are developed. It is shown through analysis and simulation that the SPCA can overcome the masking effect of nuisance features and pick up the important variables and major components. Its application to gene expression data analysis is also demonstrated.
Jiayang Sun, PhD (Advisor)
Joe Sedransk, PhD (Committee Member)
Guoqiang Zhang, PhD (Committee Member)
Mark Schluchter, PhD (Committee Member)
Patricia Williamson, PhD (Committee Member)
119 p.

Recommended Citations

Citations

  • Liu, P. (2009). Adaptive Mixture Estimation and Subsampling PCA [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

    APA Style (7th edition)

  • Liu, Peng. Adaptive Mixture Estimation and Subsampling PCA. 2009. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686.

    MLA Style (8th edition)

  • Liu, Peng. "Adaptive Mixture Estimation and Subsampling PCA." Doctoral dissertation, Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

    Chicago Manual of Style (17th edition)