rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Principal components analysis for binary data
  name:
    name part: Lee, Seokho.
    role:
      role term: creator
  name:
    name part: Carroll, Raymond J.
    role:
      role term: thesis advisor.
  name:
    name part: Huang, Jianhua Z.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Principal components analysis (PCA) has been widely used as a statistical tool for the dimension reduction of multivariate data in various application areas and extensively studied in the long history of statistics. One of the limitations of PCA machinery is that PCA can be applied only to the continuous type variables. Recent advances of information technology in various applied areas have created numerous large diverse data sets with a high dimensional feature space, including high dimensional binary data. In spite of such great demands, only a few methodologies tailored to such binary dataset have been suggested. The methodologies we developed are the model-based approach for generalization to binary data. We developed a statistical model for binary PCA and proposed two stable estimation procedures using MM algorithm and variational method. By considering the regularization technique, the selection of important variables is automatically achieved. We also proposed an efficient algorithm for model selection including the choice of the number of principal components and regularization parameter in this study.
  note: by Seokho Lee.
  note: "Major Subject: Statistics"
  note: Title from author supplied metadata (automated record created 2010-08-20 08:31:34).
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2009 http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major statistics
  subject:
    topic: Variational method
  subject:
    topic: Regularization
  subject:
    topic: MM algorithm
  subject:
    topic: PCA
  subject:
    topic: Dimension reduction
  subject:
    topic: Binary data
  subject:
    topic: Lasso
  subject:
    topic: Spasity
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2009-05-602
  record info:
    record content source: TXA
    record creation date: 100915
    record change date: 20151201155337.0
    record identifier: in00002609568