Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Variational relevance vector machines
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Selection of Subsets of Ordered Features in Machine Learning
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Combining pattern recognition modalities at the sensor level via kernel fusion
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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The scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for dissimilarity-based processing of data in metric spaces representing distances between entities, for which sufficiently informative features cannot be found, is much poorer. In this work, the problem of embedding the given set of entities into a linear space with inner product by choosing an appropriate kernel function is considered as the major challenge in the featureless approach to estimating dependences in data sets of arbitrary kind. As a rule, several kernels may be heuristically suggested within the bounds of the same data analysis problem. We treat several kernels on a set of entities as Cartesian product of the respective number of linear spaces, each supplied with a specific kernel function as a specific inner product. The main requirement here is to avoid discrete selection in eliminating redundant kernels with the purpose of achieving acceptable computational complexity of the fusion algorithm.