title: Feature selection and multi-kernel learning for sparse representation on a manifold.
abstract: Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods.
authors: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Electronic address: jimjywang@gmail.com.
authors: Jim Jing-Yan
authors: JJ
authors: Wang
authors: Qatar Computing Research Institute, Doha 5825, Qatar.
authors: Halima
authors: H
authors: Bensmail
authors: Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. Electronic address: xin.gao@kaust.edu.sa.
authors: Xin
authors: X
authors: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D006031:Glycosylation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008327:Mammography; D017711:Nonlinear Dynamics; D012372:ROC Curve
keywords: Data representation; Feature selection; Manifold; Multiple kernel learning; Sparse coding