ISSN 
一种非负稀疏近邻表示的多标签学习算法
详细信息A Non-Negative Sparse Neighbor Representation for Multi-Label Learning Algorithm
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摘要: 针对训练数据中的非线性流形结构以及基于稀疏表示的多标签分类中判别信息丢失严重的问题,该文提出一种非负稀疏近邻表示的多标签学习算法。首先找到待测试样本每个标签类上的k-近邻,然后基于LASSO稀疏最小化方法,对待测试样本进行非负稀疏线性重构,得到稀疏的非负重构系数。再根据重构误差计算待测试样本对每个类别的隶属度,最后实现多标签数据分类。实验结果表明所提出的方法比经典的多标签k近邻分类(ML-KNN)和稀疏表示的多标记学习算法(ML-SRC)方法性能更优。
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关键词:
- 多标签学习 /
- 稀疏近邻表示 /
- LASSO稀疏最小化 /
- 非负重构
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[1] Schapire R E, Singer Y. Boostexter: a boosting-based system for text categorization[J]. Machine Learning, 2000, 39(2-3): 135-168.
[2] Ueda N, Saito K. Parametric mixture models for multi-label text[J]. Advances in Neural Information Processing, 2003(15): 721-728.
[3] Zhang M L, Zhou Z H. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048.
[4] Sanden C, Zhang J Z. Enhancing multi-label music genre classification through ensemble techniques[C] //Proceedings of the 34th international ACM SIGIR Conference on Research and development in Information Retrieval. New York: ACM, 2011: 705-714.
[5] Elisseeff A, Weston J. A kernel method for multi-labelled classification[J]. Advances in Neural Information Processing, 2002(14): 681-687.
[6] Candès E J, Romberg J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
[7] Wright J, Yang A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[8] JI Y, LIN T, ZHA H. Mahalanobis distance based non-negative sparse representation for face recognition[C]// International Conference on Machine Learning and Applications. Miami, FL: IEEE, 2009: 41-46.
[9] Hui K, Li C, Zhang L. Sparse neighbor representation for classification[J]. Pattern Recognition Letters, 2012, 33(5): 661-669.
[10] 宋相法, 焦李成. 基于稀疏表示的多标记学习算法[J]. 模式识别与人工智能, 2012, 25(1): 124-129. SONG Xiang-fa, JIAO Li-cheng. A multi-label learning algorithm based on sparse representation[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(1): 124-129. [11] Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society (Series B, Methodological), 1996, 58(1): 67-88.
[12] Lee D D, Seung H S. Algorithms for non-negative matrix factorization[J]. Advances in Neural Information Processing, 2001(2): 556-562.
[13] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202.
[14] Boutell M R, Luo J, Shen X, et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37(9): 1757-1771.
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