基于PLS的信息特征压缩算法

提出了基于偏最小二乘(PLS)方法的信息特征压缩算法．较主成分分析(PCA)方法，该算法具有简单、稳健、易于定性解释等优点，对于多重共线性资料，尤其当解释变量多，而样本量少时很有效．由于在考虑压缩数据矩阵X的信息的同时，顾及了与目标矩阵Y的最大相关性等优点，使之更符合实际．数值实例研究表明，文中算法是可行的、有效的，为模式识别的信息特征压缩提供了一种新的研究方法．

Information Feature Compression Based on Partial Least Squares

Partial least squares (PLS) regression is introduced for information feature compression, which is proven to be more advantageous than the approach of principal component analysis (PCA) in its simplicity,robustness, and clearness of qualitative explanation. It is powerful for processing multicollinear information, particularly when the number of predictor variables is large and the sample size is small. Meanwhile it is more effective than PCA, when the information of data matrix X is compressed while still maintaining their maximum correlation with objective matrix Y. Numerical example shows that the algorithm is feasible and effective, and provides a new research approach for information feature compression in pattern recognition.