High-dimensional Distance Metric Learning for Ordinal Classification

01 October 2015 → 30 September 2019
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Artificial intelligence
    • Computer architecture and networks
    • Distributed computing
    • Information sciences
    • Information systems
    • Programming languages
    • Scientific computing
    • Theoretical computer science
    • Visual computing
    • Other information and computing sciences
  • Social sciences
    • Cognitive science and intelligent systems
  • Engineering and technology
    • Computer hardware
    • Computer theory
    • Scientific computing
    • Other computer engineering, information technology and mathematical engineering
machine learning ordinal classification distance metric learning
Project description

Much like in other modelling disciplines does the distance metric used (a measure for dissimilarity) play an important role in the growing field of machine learning. Not surprisingly, in this field one also tries to learn the distance metric. In classification problems this has led to a dramatic performance boost. In this proposal we will
develop this learning methodology for ordinal classification problems (an important problem setting between classification and regression), with special attention for high-dimensional data, as they are often available nowadays.