| 摘要: |
| 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来提高分类性能. |
| 关键词: 典型相关分析 半监督学习 成对约束 降维 分类 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60505004, 60875030 (国家自然科学基金); the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2006521 (江苏省自然科学基金) |
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| Semi-Supervised Canonical Correlation Analysis Algorithm |
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PENG Yan,ZHANG Dao-Qiang
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| Abstract: |
| 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. |
| Key words: canonical correlation analysis semi-supervised learning pair-wise constraints dimensionality reduction classification |
