Discriminative sparse coding on multi-manifolds
Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold–manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach.
Other Information
Published in: Knowledge-Based Systems
License: http://creativecommons.org/licenses/by-nc-sa/3.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.knosys.2013.09.004
Funding
Nanjing University, National Key Laboratory for Novel Software Technology (KFKT2012B17).
2011 Qatar Annual Research Forum Award (ARF2011).
King Abdullah University of Science and Technology - KAUST, Saudi Arabia (N/A).
History
Language
- English
Publisher
ElsevierPublication Year
- 2013
License statement
This Item is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International.Institution affiliated with
- Hamad Bin Khalifa University
- Qatar Computing Research Institute - HBKU