Multi-Output Learning via Spectral Filtering
Abstract
In this paper we study a class of regularized kernel methods for multi-output learning which are based on filtering the spectrum of the kernel matrix. The considered methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector-valued extensions of L2 boosting and other iterative schemes. Computational properties are discussed for various examples of kernels for vector-valued functions and the benefits of iterative techniques are illustrated. Generalizing previous results for the scalar case, we show a finite sample bound for the excess risk of the obtained estimator, which allows to prove consistency both for regression and multi-category classification. Finally, we present some promising results of the proposed algorithms on artificial and real data.
Cite
Text
Baldassarre et al. "Multi-Output Learning via Spectral Filtering." Machine Learning, 2012. doi:10.1007/S10994-012-5282-YMarkdown
[Baldassarre et al. "Multi-Output Learning via Spectral Filtering." Machine Learning, 2012.](https://mlanthology.org/mlj/2012/baldassarre2012mlj-multioutput/) doi:10.1007/S10994-012-5282-YBibTeX
@article{baldassarre2012mlj-multioutput,
title = {{Multi-Output Learning via Spectral Filtering}},
author = {Baldassarre, Luca and Rosasco, Lorenzo and Barla, Annalisa and Verri, Alessandro},
journal = {Machine Learning},
year = {2012},
pages = {259-301},
doi = {10.1007/S10994-012-5282-Y},
volume = {87},
url = {https://mlanthology.org/mlj/2012/baldassarre2012mlj-multioutput/}
}