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SENSC: a Stable and Efficient Algorithm for Nonnegative Sparse Coding
Abstract
摘要: 非负稀疏编码(Nonnegative sparse coding, NSC)已成功应用在很多领域的研究中. 目前使用的NSC算法通过梯度投影法和基于辅助函数的乘性更新法相结合来实现, 其性能受迭代步长的影响很大, 且效率较低. 为增强NSC的可应用性, 本文通过对一组凸超抛物面函数做交替最小化来实现NSC, 并依据凸超抛物面特性、点到非负数集合的投影规则以及点到原点处单位超球的投影规则构造了一个无用户定义优化参数的稳定高效的NSC算法---SENSC. 从数学角度, 文中推断了SENSC比现有算法高效且它的解优于当前算法的解, 证明了它的稳定性和收敛性. 实验验证了上述理论推断的正确, 说明了SENSC调节编码稀疏性的能力比已有算法更强. 关键词: 非负稀疏编码 / 非负矩阵分解 / 稀疏编码 / 超抛物面
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Cites
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
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Nonnegative Matrix Factorization: A Comprehensive Review
Wang, Y.-X., & Zhang, Y.-J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.51 · Cited by 981
Self-citationLe, Q. V., Zou, W. Y., Yeung, S. Y., & Ng, A. Y. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995496 · Cited by 960
Online detection of unusual events in videos via dynamic sparse coding
Zhao, B., Fei-Fei, L., & Xing, E. P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995524 · Cited by 532
Fast Convolutional Sparse Coding
Bristow, H., Eriksson, A., & Lucey, S. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.57 · Cited by 329
Metadata
- Authors
- LI, L., & ZHANG, Y.-J.
- Venue
- Acta Automatica Sinica
- Published
- 2009-11-13
- Volume
- 35
- Issue
- 10
- Pages
- 1257-1271
- ISSN
- 0254-4156 (ISSN-L)
- OpenAlex
- W1989975849
- Cited by
- 223
- References
- 1
- FWCI
- 34.06
- Type
- journal article
- Language
- en
- Open access
- closed
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