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    Asymptotic Performance Analysis of the Randomly-Projected RLDA Ensemble Classifier

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    Name:
    LamaNiyazi_MasterThesis.pdf
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    662.2Kb
    Format:
    PDF
    Description:
    Final thesis
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    Type
    Thesis
    Authors
    Niyazi, Lama cc
    Advisors
    Alouini, Mohamed-Slim cc
    Committee members
    Al-Naffouri, Tareq Y. cc
    Kammoun,Alba
    Dahrouj, Hayssam cc
    Program
    Electrical and Computer Engineering
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2019-07
    Permanent link to this record
    http://hdl.handle.net/10754/655994
    
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    Abstract
    Reliability and computational efficiency of classification error estimators are critical factors in classifier design. In a high-dimensional data setting where data is scarce, the conventional method of error estimation, cross-validation, can be very computationally expensive. In this thesis, we consider a particular discriminant analysis type classifier, the Randomly-Projected RLDA ensemble classifier, which operates under the assumption of such a ‘small sample’ regime. We conduct an asymptotic study of the generalization error of this classifier under this regime, which necessitates the use of tools from the field of random matrix theory. The main outcome of this study is a deterministic function of the true statistics of the data and the problem dimension that approximates the generalization error well for large enough dimensions. This is demonstrated by simulation on synthetic data. The main advantage of this approach is that it is computationally efficient. It also constitutes a major step towards the construction of a consistent estimator of the error that depends on the training data and not the true statistics, and so can be applied to real data. An analogous quantity for the Randomly-Projected LDA ensemble classifier, which appears in the literature and is a special case of the former, is also derived. We motivate its use for tuning the parameter of this classifier by simulation on synthetic data.
    Citation
    Niyazi, L. (2019). Asymptotic Performance Analysis of the Randomly-Projected RLDA Ensemble Classifier. KAUST Research Repository. https://doi.org/10.25781/KAUST-351Z3
    DOI
    10.25781/KAUST-351Z3
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-351Z3
    Scopus Count
    Collections
    MS Theses; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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