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中文摘要：
针对弱匹配多模态数据的相关性建模问题,提出了一种弱匹配概率典型相关性分析模型（semi-paired probabilistic CCA,简称Semi PCCA）.Semi PCCA模型关注于各模态内部的全局结构,模型参数的估计受到了未匹配样本的影响,而未匹配样本则揭示了各模态样本空间的全局结构.在人工弱匹配多模态数据集上的实验结果表明,Semi PCCA可以有效地解决传统CCA（canonical correlation analysis）和PCCA（probabilistic CCA）在匹配样本不足的情况下出现的过拟合问题,取得了较好的效果.提出了一种基于Semi PCCA的图像自动标注方法.该方法基于关联建模的思想,同时使用标注图像及其关键词和未标注图像学习视觉模态和文本模态之间的关联,从而能够更准确地对未知图像进行标注.
英文摘要：
Canonical correlation analysis（CCA） is a statistical analysis tool for analyzing the correlation between two sets of random variables. CCA requires the data be rigorously paired or one-to-one correspondence among different views due to its correlation definition. However, such requirement is usually not satisfied in real-world applications due to various reasons. Often, only a few paired and a lot of unpaired multi-view data are given, because unpaired multi-view data are relatively easier to be collected and pairing them is difficult, time consuming and even expensive. Such data is referred as semi-paired multi-view data. When facing semi-paired multi-view data, CCA usually performs poorly. To tackle this problem, a semi-paired variant of CCA, named Semi PCCA, is proposed based on the probabilistic model for CCA. The actual meaning of ＂semi-＂ in Semi PCCA is ＂semi-paired＂ rather than ＂semi-supervised＂ as in popular semi-supervised learning literature. The estimation of Semi PCCA model parameters is affected by the unpaired multi-view data which reveal the global structure within each modality. By using artificially generated semi-paired multi-view data sets, the experiment shows that Semi PCCA effectively overcome the over-fitting problem of traditional CCA and PCCA（probabilistic CCA） under the condition of insufficient paired multi-view data and performs better than the original CCA and PCCA. In addition, an automatic image annotation method based on the Semi PCCA is presented. Through estimating the relevance between images and words by using the labelled and unlabeled images together, this method is shown to be more accurate than previous published methods.
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