Discriminative Interpolation for Classification of Functional Data

The

modus operandi

for machine learning is to represent data as feature vectors and then proceed with training algorithms that seek to optimally partition the feature space

$$S\subset {\mathbb {R}}^{n}$$

into labeled regions. This holds true even when the original data are functional in nature, i.e. curves or surfaces that are inherently varying over a continuum such as time or space. Functional data are often reduced to summary statistics, locally-sensitive characteristics, and global signatures with the objective of building comprehensive feature vectors that uniquely characterize each function. The present work directly addresses representational issues of functional data for supervised learning. We propose a novel

classification by discriminative interpolation

(CDI)

framework wherein functional data in the same class are adaptively reconstructed to be more similar to each other, while simultaneously repelling nearest neighbor functional data in other classes. Akin to other recent nearest-neighbor metric learning paradigms like stochastic

k

-neighborhood selection and large margin nearest neighbors, our technique uses class-specific representations which gerrymander similar functional data in an appropriate parameter space. Experimental validation on several time series datasets establish the proposed discriminative interpolation framework as competitive or better in comparison to recent state-of-the-art techniques which continue to rely on the standard feature vector representation.