Partial least-squares Regression with Unlabeled Data

摘要：

It is well known that the prediction errors from principal component regression (PCR) and partial least-squares regression (PLSR) can be reduced by using both labeled and unlabeled data for stabilizing the latent subspaces in the calibration step. An approach using Kalman Filtering has been proposed to optimally use unlabeled data with PLSR. In this work, a sequential version of this optimized PLSR as well as two new PLSR models with unlabeled data, namely PCA-based PLSR (PLSR applied to PCA-preprocessed data) and imputation PLSR (iterative procedure to impute the missing labels), are proposed. It is shown analytically and verified with both simulated and real data that the sequential version of the optimized PLSR is equivalent to PCA-based PLSR.

关键词： Multivariate calibration labeled data unlabeled data Kalman filtering imputation

作者: Paman Gujral Barry Wise Michael Amrhein Dominique Bonvin

作者单位: Laboratoire dAutomatique, Ecole Polytechnique Federate de Lausanne,1015 Lausanne, Switzerland Eigenvector Research Inc., 3905 West Eaglerock Drive, Wenatchee 98801, USA

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 102-105

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）