Semi-supervised partial least squares
Semi-supervised partial least squares
复制标题
半监督偏最小二乘法
DOI：
10.1142/s0219691320500149
复制
发表时间：
2020-01
期刊：
影响因子：
--
通讯作者：
Qiwei Xie
中科院分区：
文献类型：
--
作者：
Xi Jin;Xing Zhang;Kaifeng Rao;Liang Tang;Qiwei Xie
Traditional supervised dimensionality reduction methods can establish a better model often under the premise of a large number of samples. However, in real-world applications where labeled data are scarce, traditional methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method by using partial least squares (PLS) which we call semi-supervised partial least squares (S2PLS). To combine the labeled and unlabeled samples into a S2PLS model, we first apply the PLS algorithm to unsupervised dimensionality reduction. Then, the final S2PLS model is established by ensembling the supervised PLS model and the unsupervised PLS model which using the basic idea of principal model analysis (PMA) method. Compared with unsupervised or supervised dimensionality reduction algorithms, S2PLS not only can improve the prediction accuracy of the samples but also enhance the generalization ability of the model. Meanwhile, it can obtain better results even there are only a few or no labeled samples. Experimental results on five UCI data sets also confirmed the above properties of S2PLS algorithm.
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DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
A. Boulesteix
通讯作者：
A. Boulesteix
影响因子：
3.7
作者：
M. Timmerman
通讯作者：
M. Timmerman
影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.
通讯作者：
Williams, Lynne J.
DOI：
10.1109/tpami.2005.55
发表时间：
2005-03-01
影响因子：
23.6
作者：
He, XF;Yan, SC;Zhang, HJ
通讯作者：
Zhang, HJ
DOI：
10.1142/s0219691319500139
发表时间：
2019-05
期刊：
Int. J. Wavelets Multiresolution Inf. Process.
影响因子：
--
作者：
Hao Deng;Chao Ma;Lijun Shen;Chuanwu Yang
通讯作者：
Hao Deng;Chao Ma;Lijun Shen;Chuanwu Yang