Title:
Theoretical Results and Applications Related to Dimension Reduction

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dc.contributor.advisor Huo, Xiaoming
dc.contributor.author Chen, Jie en_US
dc.contributor.committeeMember Deng, Shi-Jie
dc.contributor.committeeMember Li, Minqiang
dc.contributor.committeeMember Serban, Nicoleta
dc.contributor.committeeMember Wu, Jeff
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2008-02-07T18:48:36Z
dc.date.available 2008-02-07T18:48:36Z
dc.date.issued 2007-11-01 en_US
dc.description.abstract To overcome the curse of dimensionality, dimension reduction is important and necessary for understanding the underlying phenomena in a variety of fields. Dimension reduction is the transformation of high-dimensional data into a meaningful representation in the low-dimensional space. It can be further classified into feature selection and feature extraction. In this thesis, which is composed of four projects, the first two focus on feature selection, and the last two concentrate on feature extraction. The content of the thesis is as follows. The first project presents several efficient methods for the sparse representation of a multiple measurement vector (MMV); some theoretical properties of the algorithms are also discussed. The second project introduces the NP-hardness problem for penalized likelihood estimators, including penalized least squares estimators, penalized least absolute deviation regression and penalized support vector machines. The third project focuses on the application of manifold learning in the analysis and prediction of 24-hour electricity price curves. The last project proposes a new hessian regularized nonlinear time-series model for prediction in time series. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/19841
dc.publisher Georgia Institute of Technology en_US
dc.subject HRM en_US
dc.subject Time series prediction en_US
dc.subject Electricity price curve en_US
dc.subject Dimension reduction en_US
dc.subject MMV en_US
dc.subject Penalized likelihood estimator en_US
dc.subject.lcsh Computational complexity
dc.subject.lcsh Estimation theory
dc.subject.lcsh Prediction theory
dc.subject.lcsh Electricity
dc.subject.lcsh Pricing
dc.title Theoretical Results and Applications Related to Dimension Reduction en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Huo, Xiaoming
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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