The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Multi-View Intact Space Learning
Open accessAbstract
It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data. Even though each view on its own is insufficient, we show theoretically that by combing multiple views we can obtain abundant information for latent intact space learning. Employing the Cauchy loss (a technique used in statistical learning) as the error measurement strengthens robustness to outliers. We propose a new definition of multi-view stability and then derive the generalization error bound based on multi-view stability and Rademacher complexity, and show that the complementarity between multiple views is beneficial for the stability and generalization. MISL is efficiently optimized using a novel Iteratively Reweight Residuals (IRR) technique, whose convergence is theoretically analyzed. Experiments on synthetic data and real-world datasets demonstrate that MISL is an effective and promising algorithm for practical applications.
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Cites
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,235
Combining labeled and unlabeled data with co-training
Blum, A., & Mitchell, T. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279962 · Cited by 5,495
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
Bartlett, P. L., & Mendelson, S. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44581-1_15 · Cited by 1,949
Multiple Kernel Learning Algorithms
Gönen, M., & Alpaydın, E. (2011) · doi:10.5555/1953048.2021071 · Cited by 1,688
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453
The CMU Pose, Illumination, and Expression (PIE) database
Sim, T., Baker, S., & Bsat, M. (2003) · Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition · doi:10.1109/afgr.2002.1004130 · Cited by 1,419
Neural network learning theoretical foundations
Anthony, M., & Bartlett, P. L. (2009) · Cited by 1,310
Large Scale Multiple Kernel Learning
Sonnenburg, S., Rätsch, G., Schäfer, C., & Schölkopf, B. (2006) · MPG.PuRe (Max Planck Society) · doi:10.5555/1248547.1248604 · Cited by 1,201
Cited by
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
Timofte, R., Agustsson, E., Gool, L. V., Yang, M.-H., Zhang, L., Lim, B., Son, S., Kim, H., Nah, S., Lee, K. M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.149 · Cited by 1,441
Self-citationMulti-view learning overview: Recent progress and new challenges
Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002
Consistent and Specific Multi-View Subspace Clustering
Luo, S., Zhang, C., Zhang, W., & Cao, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11617 · Cited by 426
Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification
Nie, F., Cai, G., Li, J., & Li, X. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2754939 · Cited by 406
Metadata
- Authors
- Xu, C., Tao, D., & Xu, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2015-12-01
- Volume
- 37
- Issue
- 12
- Pages
- 2531-2544
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2035299679
- Cited by
- 395
- References
- 49
- FWCI
- 72.89
- Type
- journal article
- Language
- en
- Open access
- green
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