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Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Rui Wang1, Miaomiao Shen1,*, Yanping Li1, Samuel Gomes2

School of Communication and Information Engineering, Shanghai University, No. 99 Shangda Road, Shanghai, 200444, China.
School of Mechatronics, Methods, Models and Skills Laboratory (M3M), Universite de Technologie de Belfort-Montbeliard, 90010 Belfort cedex, Belfort, France.

* Corresponding Author: Miaomiao Shen. Email: .

Computers, Materials & Continua 2018, 57(1), 25-48.


Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for vehicle classification. In our proposed method, the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients (MFCC). Moreover, we extend our model to handle sparse environmental noise. We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.


Cite This Article

R. Wang, M. Shen, Y. Li and S. Gomes, "Multi-task joint sparse representation classification based on fisher discrimination dictionary learning," Computers, Materials & Continua, vol. 57, no.1, pp. 25–48, 2018.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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