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|Title:||Learning with ℓ1-graph for image analysis|
|Source:||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|
|Appears in Collections:||Staff Publications|
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