Functional Regularized Least Squares Classi cation with Operator-valued Kernels
This work addresses a specific issue in functional data analysis for machine learning applications, but it is incremental as it builds on existing RLSC methods.
The paper tackles the problem of analyzing functional data with multiple functions per observation by extending the Regularized Least Squares Classification (RLSC) algorithm using operator-valued kernels, and experiments on a sound recognition problem show that the proposed method outperforms the classical RLSC algorithm.
Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attention has been paid to the understanding of their associated feature spaces. In this paper, we explore the potential of adopting an operator-valued kernel feature space perspective for the analysis of functional data. We then extend the Regularized Least Squares Classification (RLSC) algorithm to cover situations where there are multiple functions per observation. Experiments on a sound recognition problem show that the proposed method outperforms the classical RLSC algorithm.