---
res:
  bibo_abstract:
  - We propose a new method to partition an unlabeled dataset, called Discriminative
    Context Partitioning (DCP). It is motivated by the idea of splitting the dataset
    based only on how well the resulting parts can be separated from a context class
    of disjoint data points. This is in contrast to typical clustering techniques
    like K-means that are based on a generative model by implicitly or explicitly
    searching for modes in the distribution of samples. The discriminative criterion
    in DCP avoids the problems that density based methods have when the a priori assumption
    of multimodality is violated, when the number of samples becomes small in relation
    to the dimensionality of the feature space, or if the cluster sizes are strongly
    unbalanced. We formulate DCP‘s separation
    property as a large-margin criterion, and show how the resulting optimization
    problem can be solved efficiently. Experiments on the MNIST and USPS datasets
    of handwritten digits and on a subset of the Caltech256 dataset show that, given
    a suitable context, DCP can achieve good results even in situation where density-based
    clustering techniques fail.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Christoph Lampert
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1109/CVPR.2008.4587448
  dct_date: 2008^xs_gYear
  dct_publisher: IEEE@
  dct_title: Partitioning of image datasets using discriminative context information@
...