• Login
    View Item 
    •   SMARTech Home
    • GVU Center
    • GVU Center Technical Reports
    • View Item
    •   SMARTech Home
    • GVU Center
    • GVU Center Technical Reports
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Robust Generative Subspace Modeling: The Subspace t Distribution

    Thumbnail
    View/Open
    04-11.pdf (272.3Kb)
    Date
    2004
    Author
    Khan, Zia
    Dellaert, Frank
    Metadata
    Show full item record
    Abstract
    Linear latent variable models such as statistical factor analysis (SFA) and probabilistic principal component analysis (PPCA) assume that the data are distributed according to a multivariate Gaussian. A drawback of this assumption is that parameter learning in these models is sensitive to outliers in the training data. Approaches that rely on M-estimation have been introduced to render principal component analysis (PCA) more robust to outliers. M-estimation approaches assume the data are distributed according to a density with heavier tails than a Gaussian. Yet, these methods are limited in that they fail to define a probability model for the data. Data cannot be generated from these models, and the normalized probability of new data cannot evaluated. To address these limitations, we describe a generative probability model that accounts for outliers. The model is a linear latent variable model in which the marginal density over the data is a multivariate t, a distribution with heavier tails than a Gaussian. We present a computationally efficient expectation maximization (EM) algorithm for estimating the model parameters, and compare our approach with that of PPCA on both synthetic and real data sets.
    URI
    http://hdl.handle.net/1853/60
    Collections
    • Computational Perception & Robotics [213]
    • Computational Perception & Robotics Publications [213]
    • GVU Center Technical Reports [542]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology