基于稀疏和近邻保持的极限学习机降维
|
摘要
近邻与稀疏保持投影已被广泛应用于降维方法,通过优化得到满足近邻结构或稀疏结构的降维投影矩阵,然而这类方法多数只考虑单一结构特征.此外,多数非线性降维方法无法求出显式的映射函数,极大地限制了降维方法的应用.为克服这些问题,本文借鉴极限学习机的思想,提出面向聚类的基于稀疏和近邻保持的极限学习机降维算法(SNP-ELM). SNP-ELM算法是一种非线性无监督降维方法,在降维过程中同时考虑数据的稀疏结构与近邻结构.在人造数据、Wine数据和6个基因表达数据上进行实验,实验结果表明该算法优于其他降维方法.
Neighborhood and sparsity structure preserving projections have been widely used in dimensionality reduction,but most of them consider single structures. Moreover, existing nonlinear DR methods can not get an accurate projection function, which limits their applications. To overcome these problems, we propose a nonlinear dimensionality reduction method SNP-ELM by extending the extreme learning machine model. SNP-ELM is a nonlinear unsupervised dimensionlity reduction method, which takes both sparsity structure and neighborhood structure into account. The experimental results on toy data, wine data and six gene expression data show that our method significantly outperforms the compared dimensionality reduction methods.
Neighborhood and sparsity structure preserving projections have been widely used in dimensionality reduction,but most of them consider single structures. Moreover, existing nonlinear DR methods can not get an accurate projection function, which limits their applications. To overcome these problems, we propose a nonlinear dimensionality reduction method SNP-ELM by extending the extreme learning machine model. SNP-ELM is a nonlinear unsupervised dimensionlity reduction method, which takes both sparsity structure and neighborhood structure into account. The experimental results on toy data, wine data and six gene expression data show that our method significantly outperforms the compared dimensionality reduction methods.
引文
1 Jolliffe I T. Principal Component Analysis. Berlin:SpringerVerlag, 2002.
2 He X F, Niyogi P. Locality preserving projections. In:Proceedings of 2003 Neural Information Processing Systems.Vancouver, Canada:NIPS, 2004. 153-160
3 He X F, Cai D, Yan S C, Zhang H J. Ncighborhood preserving embedding. In:Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China:IEEE, 2005. 1208-1213
4 Qiao L S, Chen S C, Tan X Y. Sparsity preserving projections with applications to face recognition. Pattern Recognition, 2010, 43(1):331-341
5 Scholkopf B, Smola A J, Müller K R. Kernel principal component analysis. In:Proceedings of the 7th International Conference on Artificial Neural Networks. Switzerland:Springer, 1997. 583-588
6 Roweis S T, Saul K L. Nonlinear dimensionality reduction by locally linear embedding. Science, 2010, 290(5500):2323-2326
7 Huang G B, Ding X J, Zhou H M. Optimization method based extreme learning machine for classification. Neurocomputing, 2010, 74(1-3):155-163
8 Peng Y, Wang S H, Long X Z, Lu B L. Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing, 2015, 149:340-353
9 Peng Y, Lu B L. Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 2016, 174:265-277
10 Zhang K, Luo M X. Outlier-robust extreme learning machine for regression problems. Neurocomputing, 2015, 151:1519-1527
11 Huang G, Song S J, Gupta J N D, Wu C. Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 2014, 44(12):2405-2417
12 Liu Zhan-Jie, Chen Xiao-Yun. Local subspace clustering.Acta Automatica Sinica, 2016, 42(8):1238-1247(刘展杰,陈晓云.局部子空间聚类.自动化学报,2016, 42(8):1238-1247)
13 Wang Wei-Wei, Li Xiao-Ping, Feng Xiang-Chu, Wang Si-Qi.A survey on sparse subspace clustering. Acta Automatica Sinica, 2015, 41(8):1373-1384(王卫卫,李小平,冯象初,王斯琪.稀疏子空间聚类综述.自动化学报,2015, 41(8):1373-1384)
14 Kasun L L C, Yang Y, Huang G B, Zhang Z Y. Dimension reduction with extreme learning machine. IEEE Transactions on Image Processing, 2016, 25(8):3906-3918
15 Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 43(1):129-159
16 Yu L, Ding C, Loscalzo S. Stable feature selection via dense feature groups. In:Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA:ACM, 2008. 803-811
17 Gene expression model selector[online], available:http://www.gems-system.org, October 9, 2017.
2 He X F, Niyogi P. Locality preserving projections. In:Proceedings of 2003 Neural Information Processing Systems.Vancouver, Canada:NIPS, 2004. 153-160
3 He X F, Cai D, Yan S C, Zhang H J. Ncighborhood preserving embedding. In:Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China:IEEE, 2005. 1208-1213
4 Qiao L S, Chen S C, Tan X Y. Sparsity preserving projections with applications to face recognition. Pattern Recognition, 2010, 43(1):331-341
5 Scholkopf B, Smola A J, Müller K R. Kernel principal component analysis. In:Proceedings of the 7th International Conference on Artificial Neural Networks. Switzerland:Springer, 1997. 583-588
6 Roweis S T, Saul K L. Nonlinear dimensionality reduction by locally linear embedding. Science, 2010, 290(5500):2323-2326
7 Huang G B, Ding X J, Zhou H M. Optimization method based extreme learning machine for classification. Neurocomputing, 2010, 74(1-3):155-163
8 Peng Y, Wang S H, Long X Z, Lu B L. Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing, 2015, 149:340-353
9 Peng Y, Lu B L. Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 2016, 174:265-277
10 Zhang K, Luo M X. Outlier-robust extreme learning machine for regression problems. Neurocomputing, 2015, 151:1519-1527
11 Huang G, Song S J, Gupta J N D, Wu C. Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 2014, 44(12):2405-2417
12 Liu Zhan-Jie, Chen Xiao-Yun. Local subspace clustering.Acta Automatica Sinica, 2016, 42(8):1238-1247(刘展杰,陈晓云.局部子空间聚类.自动化学报,2016, 42(8):1238-1247)
13 Wang Wei-Wei, Li Xiao-Ping, Feng Xiang-Chu, Wang Si-Qi.A survey on sparse subspace clustering. Acta Automatica Sinica, 2015, 41(8):1373-1384(王卫卫,李小平,冯象初,王斯琪.稀疏子空间聚类综述.自动化学报,2015, 41(8):1373-1384)
14 Kasun L L C, Yang Y, Huang G B, Zhang Z Y. Dimension reduction with extreme learning machine. IEEE Transactions on Image Processing, 2016, 25(8):3906-3918
15 Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 43(1):129-159
16 Yu L, Ding C, Loscalzo S. Stable feature selection via dense feature groups. In:Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA:ACM, 2008. 803-811
17 Gene expression model selector[online], available:http://www.gems-system.org, October 9, 2017.