Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2009.2038764
Title: Learning with ℓ1-graph for image analysis
Authors: Cheng, B.
Yang, J.
Yan, S. 
Fu, Y.
Huang, T.S.
Keywords: Graph embedding
Semi-supervised learning
Sparse representation
Spectral clustering
Subspace learning
Issue Date: Apr-2010
Citation: Cheng, B., Yang, J., Yan, S., Fu, Y., Huang, T.S. (2010-04). Learning with ℓ1-graph for image analysis. IEEE Transactions on Image Processing 19 (4) : 858-866. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2009.2038764
Abstract: The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-called directed ℓ1-graph, in which the vertices involve all the samples and the ingoing edge weights to each vertex describe its ℓ1;-norm driven reconstruction from the remaining samples and the noise. Then, a series of new algorithms for various machine learning tasks, e.g., data clustering, subspace learning, and semi-supervised learning, are derived upon the ℓ1-graphs. Compared with the conventional k-nearest-neighbor graph and ε-ball graph, the ℓ1-graph possesses the advantages: 1) greater robustness to data noise, 2) automatic sparsity, and 3) adaptive neighborhood for individual datum. Extensive experiments on three real-world datasets show the consistent superiority of ℓ1 -graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks. © 2006 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/82618
ISSN: 10577149
DOI: 10.1109/TIP.2009.2038764
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