Abstract

Functional data is ubiquitous in many domains such as healthcare, social media, manufacturing process, sensor networks, etc. Functional data analysis involves the analysis of data which is treated as infinite-dimensional continuous functions rather than discrete, finite-dimensional vectors. In this paper, we propose a novel function-on-function regression model based on mode-sparsity regularization. The main idea is to represent the regression coefficient function between predictor and response as the double expansion of basis functions, and then use mode-sparsity constraint to automatically filter out the irrelevant basis functions for both predictors and responses. The mode-sparsity regularization covers a wide spectrum of sparse models for function-on-function regression. The resulting optimization problem is challenging due to the non-smooth property of the mode-sparsity. We develop an efficient and convergence-guaranteed algorithm to solve the problem. The effectiveness of the proposed approach is verified on benchmark functional data sets in various domains.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1311-1316
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yang, P., & He, J. (2017). Functional regression with mode-sparsity constraint. In Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016 (pp. 1311-1316). [7837991] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2016.68