The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Semi-supervised Learning by Sparse Representation
Abstract
In this paper, we present a novel semi-supervised learning framework based on ℓ1 graph. The ℓ1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed ℓ1 graph, are derived by solving an ℓ1 optimization problem on sparse representation. Different from conventional graph construction processes which are generally divided into two independent steps, i.e., adjacency searching and weight selection, the graph adjacency structure as well as the graph weights of the ℓ1 graph is derived simultaneously and in a parameter-free manner. Illuminated by the validated discriminating power of sparse representation in [16], we propose a semi-supervised learning framework based on ℓ1 graph to utilize both labeled and unlabeled data for inference on a graph. Extensive experiments on semi-supervised face recognition and image classification demonstrate the superiority of our proposed semi-supervised learning framework based on ℓ1 graph over the counterparts based on traditional graphs.
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Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Robust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
Goodall, C. (1988) · Technometrics · doi:10.1080/00401706.1988.10488412 · Cited by 8,675
Learning with Local and Global Consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2003) · MPG.PuRe (Max Planck Society) · Cited by 3,864
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X., Zoubin Ghahramani, A., & Lafferty, J. (2003) · Cited by 3,509
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., & Lin, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250598 · Cited by 3,031
Self-citationHigh dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · Cited by 2,539
Cited by
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Visual Classification With Multitask Joint Sparse Representation
Yuan, X.-T., Liu, X., & Yan, S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2205006 · Cited by 364
Self-citationConstructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Peng, X., Yu, Z., Yi, Z., & Tang, H. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2536752 · Cited by 257
Metadata
- Authors
- Yan, S., & Wang, H.
- Venue
- Proceedings of the 2009 SIAM International Conference on Data Mining
- Published
- 2009-04-30
- Pages
- 792-801
- OpenAlex
- W38891395
- Cited by
- 266
- References
- 16
- FWCI
- 14.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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