Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 20-36

Discriminative Interpolation for Classification of Functional Data

Conference paper

DOI: 10.1007/978-3-319-23528-8_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9284)
Cite this paper as:
Haber R., Rangarajan A., Peter A.M. (2015) Discriminative Interpolation for Classification of Functional Data. In: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9284. Springer, Cham


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.


Functional data classification Wavelets Discriminative Interpolation 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rana Haber
    • 1
  • Anand Rangarajan
    • 2
  • Adrian M. Peter
    • 3
  1. 1.Mathematical Sciences DepartmentFlorida Institute of TechnologyMelbourneUSA
  2. 2.Department of Computer and Information Science and EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Systems Engineering DepartmentFlorida Institute of TechnologyMelbourneUSA

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