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Extension to Kernel Dependency Estimation with Applications to Robotics




Kernel Dependency Estimation(KDE) is a novel technique which was designed to learn mappings between sets without making assumptions on the type of the involved input and output data. It learns the mapping in two stages. In a first step, it tries to estimate coordinates of a feature space representation of elements of the set by solving a high dimensional multivariate regression problem in feature space. Following this, it tries to reconstruct the original representation given the estimated coordinates. This thesis introduces various algorithmic extensions to both stages in KDE. One of the contributions of this thesis is to propose a novel linear regression algorithm that explores low-dimensional subspaces during learning. Furthermore various existing strategies for reconstructing patterns from feature maps involved in KDE are discussed and novel pre-image techniques are introduced. In particular, pre-image techniques for data-types that are of discrete nature such as graphs and strings are investigated. KDE is then explored in the context of robot pose imitation where the input is a an image with a human operator and the output is the robot articulated variables. Thus, using KDE, robot pose imitation is formulated as a regression problem.

Author(s): BakIr, G.
Year: 2005
Month: November
Day: 0

Department(s): Empirical Inference
Bibtex Type: Thesis (phdthesis)

School: Biologische Kybernetik

Degree Type: PhD
Digital: 0
Institution: Technische Universität Berlin, Berlin
Language: en

Links: PDF


  title = {Extension to Kernel Dependency Estimation with Applications to Robotics},
  author = {BakIr, G.},
  institution = {Technische Universität Berlin, Berlin},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2005},
  month_numeric = {11}