KUKU

KU ScholarWorks

  • myKU
  • Email
  • Enroll & Pay
  • KU Directory
    • Login
    View Item 
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Learning to Measure: Distance Metric Learning with Structured Sparsity

    Thumbnail
    View/Open
    Available after: 2019-05-31 (19.47Mb)
    Issue Date
    2018-05-31
    Author
    St.Amand, Joseph
    Publisher
    University of Kansas
    Format
    126 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
    Metadata
    Show full item record
    Abstract
    Many important machine learning and data mining algorithms rely on a measure to provide a notion of distance or dissimilarity. Naive metrics such as the Euclidean distance are incapable of leveraging task-specific information, and consider all features as equal. A learned distance metric can become much more effective by honing in on structure specific to a task. Additionally, it is often extremely desirable for a metric to be sparse, as this vastly increases the ability to interpret or explain the measures produced by the distance metric. In this dissertation, we explore several current problems in distance metric learning and put forth solutions which make use of structured sparsity. The contributions of this dissertation may be broadly divided into two portions. In the first portion (chapter 2) we begin with a classic approach in distance metric learning and address a scenario where distance metric learning is typically inapplicable, i.e., the case of learning on heterogeneous data in a high-dimensional input space. We construct a projection-free distance metric learning algorithm which utilizes structured sparse updates and successfully demonstrate its application to learn a metric with over a billion parameters. The second portion (chapters 3 & 4) of this dissertation focuses on a new and intriguing regression-based approach to distance metric learning. Under this regression approach there are two sets of parameters to learn; those which parameterize the metric, and those defining the so-called ``virtual points''. We begin with an exploration of the metric parameterization and develop a structured sparse approach to robustify the metric to noisy, corrupted, or irrelevant data. We then focus on the virtual points and develop a new method for learning the metric and constraints together in a simultaneous manner. We demonstrate through empirical means that our approach results in a distance metric which is much more effective than the current state of-the-art. Machine learning algorithms have recently become ingrained in an incredibly diverse amount of technology. The primary focus of this dissertation is to develop more effective techniques to learn a distance metric. We believe that this work has the potential for rather broad-reaching impacts, as learning a more effective metric typically results in more accurate metric-based machine learning algorithms.
    URI
    http://hdl.handle.net/1808/27811
    Collections
    • Engineering Dissertations and Theses [1055]
    • Dissertations [4472]

    Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.


    We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.


    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    Browse

    All of KU ScholarWorksCommunities & CollectionsThis Collection

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    The University of Kansas
      Contact KU ScholarWorks
    Lawrence, KS | Maps
     
    • Academics
    • Admission
    • Alumni
    • Athletics
    • Campuses
    • Giving
    • Jobs

    The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

     Contact KU
    Lawrence, KS | Maps