Function approximation on non-Euclidean spaces
Neural Networks
Neural Networks - 2005 Special issue: IJCNN 2005
A survey of kernel and spectral methods for clustering
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection and Kernel Learning for Local Learning-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed static linear Gaussian models using consensus
Neural Networks
Hierarchical kernel spectral clustering
Neural Networks
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
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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.