Abstract
In this paper, we propose a novel sparse learning model, named Two-Sided Sparse Learning with Augmented Lagrangian Method, and apply it to the classification problem. Existing dictionary learning method only emphasizes the sparsity of cases, but neglect the sparsity of features. In the context of classification, it is crucial to take into account the correlation among features and find the most representative features in a class. By representing training data as sparse linear combination of rows and columns in dictionary, this model can be more suitable for classification problem. Experimental results demonstrate that our model achieves superior performance than the state-of-the-art classification methods on real-world datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Monika, R., Dhanalakshmi, S., Sreejith, S.: Coefficient random permutation based compressed sensing for medical image compression. Advances in Electronics, Communication and Computing. Springer, Singapore (2018)
Gibson, R.M., Amira, A., Ramzan, N., et al.: Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomed. Signal Process. Control. (2017)
Peller, J., Thompson, K.J., Siddiqui, I., et al.: Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo. Optical Biopsy XV: Toward real-time spectroscopic imaging and diagnosis. Int. Soc. Opt. Photonics (2017)
Wright, J., Yang, M., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31(2), 210–227 (2009)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: design of dictionaries for sparse representation. Proceedings of SPARS 5, 9–12 (2005)
Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. Image Process. 54, 4311–4322 (2006)
Boyd, S., Parikh, N., Chu, E., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning (2011)
Acknowledgements
This research was supported in part by the Chinese National Natural Science Foundation under Grant nos. 61402395, 61472343 and 61502412, Natural Science Foundation of Jiangsu Province under contracts BK20140492, BK20151314 and BK20150459, Jiangsu overseas research and training program for university prominent young and middle-aged teachers and presidents, Jiangsu government scholarship funding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Xu, X. et al. (2020). Two-Sided Sparse Learning with Augmented Lagrangian Method. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-04946-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04945-4
Online ISBN: 978-3-030-04946-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)