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case1220644686.pdf (555.11 KB)
ETD Abstract Container
Abstract Header
Adaptive Mixture Estimation and Subsampling PCA
Author Info
Liu, Peng
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686
Abstract Details
Year and Degree
2009, Doctor of Philosophy, Case Western Reserve University, Sciences.
Abstract
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.
Committee
Jiayang Sun, PhD (Advisor)
Joe Sedransk, PhD (Committee Member)
Guoqiang Zhang, PhD (Committee Member)
Mark Schluchter, PhD (Committee Member)
Patricia Williamson, PhD (Committee Member)
Pages
119 p.
Subject Headings
Statistics
Keywords
large data
;
data mining
;
mixture models
;
Gaussian mixtures
;
parameter estimation
;
adaptive procedure
;
partial EM
;
high-dimensional data
;
large p small n
;
dimension reduction
;
feature selection
;
subsampling
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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)
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Document number:
case1220644686
Download Count:
882
Copyright Info
© 2008, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.