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.
CITATION STYLE
Krasotkina, O., Seredin, O., & Mottl, V. (2015). Supervised selective combination of diverse object-representation modalities for regression estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9132, pp. 89–99). Springer Verlag. https://doi.org/10.1007/978-3-319-20248-8_8
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