Abstract
We consider the problem of multi-modal regression estimation under the assumption that a kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensors is employed as an appropriate joint scale corresponding to the idea of combining modalities at the sensor level. This contrasts with the commonly adopted method of combining classifiers inferred from each specific modality. However, a significant risk in combining linear spaces is that of overfitting. To address this, we set out a stochastic method for encompassing modal-selectivity that is intrinsic to (that is to say, theoretically contiguous with) the selected kernel-based approach.
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Notes
- 1.
It is incorrect to speak about strictly normal densities since the dimensionality of each linear space \( \tilde{\Omega }_{i} \) depends on the respective kernel function. As a result, the normalization coefficient of any density \( \left( {\uppsi_{i} (a_{i} ),\;a_{i} \, \in \,\tilde{\Omega }_{i} } \right) \) cannot be specified before the kernel is completely defined.
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This research is funded by RFBR, grants 14-07-00964, 14-07-00527.
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Krasotkina, O., Seredin, O., Mottl, V. (2015). Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_8
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