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Conformal Multi-Instance Kernels


Conference Paper


In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.

Author(s): Blaschko, MB. and Hofmann, T.
Journal: NIPS 2006 Workshop on Learning to Compare Examples
Pages: 1-6
Year: 2006
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: NIPS 2006 Workshop on Learning to Compare Examples
Event Place: Whistler, BC, Canada

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Conformal Multi-Instance Kernels},
  author = {Blaschko, MB. and Hofmann, T.},
  journal = {NIPS 2006 Workshop on Learning to Compare Examples},
  pages = {1-6},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2006},
  month_numeric = {12}