Discriminative concept factorization for data representation
Neurocomputing
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
Correlation preserved dictionary learning for sparse representation
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Letters: Enhancing sparsity via ℓp (0
Neurocomputing
Sparse functional representation for large-scale service clustering
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Graph regularized ICA for over-complete feature learning
CVM'12 Proceedings of the First international conference on Computational Visual Media
Learning dictionary on manifolds for image classification
Pattern Recognition
Sparse hashing for fast multimedia search
ACM Transactions on Information Systems (TOIS)
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
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Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.