半监督典型相关分析算法

在典型相关分析算法(canonical correlation analysis,简称CCA)的基础上,通过引入以成对约束形式给出的监督信息,提出了一种半监督的典型相关分析算法(Semi-CCA).在此算法中,除了考虑大量的无标号样本以外,还考虑成对约束信息,即已知两样本属于同一类(正约束)或不属于同一类(负约束),同时验证了两者的相对重要性.在人工数据集、多特征手写体数据集和人脸数据集(Yale和AR)上的实验结果表明,Semi-CCA能够有效地利用少量的监督信息采提高分类性能.

Semi-Supervised Canonical Correlation Analysis Algorithm

In this paper,a semi-supervised canonical correlation analysis algorithm called Semi-CCA is developed, which uses supervision information in the form of pair-wise constraints in canonical correlation analysis (CCA).In this setting,besides abundant unlabeled data examples,the domain knowledge in the form of pair-wise constraints which specify whether a pair of data examples belongs to the same class (must-link constraints) or not (cannot-link constraints) is also available.Meanwhile,the relative importance of must-link constraints and cannot-link constraints is validated.Experimental results on the artificial dataset,multiple feature database and facial database including Yale and AR show that the proposed Semi-CCA can effectively enhance the classifier performance by using only a small amount of supervision information.