Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2020

Abstract

This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on the other hand is able to handle weakly correlated functions with different features. Furthermore, the method incorporates time warping into factorization and hence allows the extracted basis functions of different samples to have temporal deformations. An efficient framework of estimation algorithms is proposed based on a greedy variable selection approach. Numerical studies together with case studies on real-world data demonstrate the efficacy and applicability of the proposed methodology.

Keywords

Non-negative functional factorization, multivariate functional data, sparse representation, time warping

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

14

Issue

6

First Page

1

Last Page

23

ISSN

1556-4681

Identifier

10.1145/3408313

Publisher

Association for Computing Machinery (ACM)

Comments

Cited by: 2

Additional URL

https://doi.org/10.1145/3408313

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