TY - GEN
T1 - Functional regression with mode-sparsity constraint
AU - Yang, Pei
AU - He, Jingrui
N1 - Funding Information:
This work is supported by the NSF research grant IIS-1552654, ONR Research grant N00014- 15-1-2821, IBM Faculty Award, and NSFC research grant 61473123. The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agencies or the governments.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
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U2 - 10.1109/ICDM.2016.68
DO - 10.1109/ICDM.2016.68
M3 - Conference contribution
AN - SCOPUS:85014531631
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1311
EP - 1316
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -