多示例学习中, 包空间特征描述包容易忽略包中的局部信息, 示例空间特征描述包容易忽略包的整体结构信息. 针对上述问题, 提出融合包空间特征和示例空间特征的多示例学习方法. 首先建立图模型表达包中示例之间的关系, 将图模型转化为关联矩阵以构建包空间特征; 其次筛选出正包中与正包的类别的相关性比较强的示例和负包中与正包的类别的相关性比较弱的示例, 将示例特征分别作为正包和负包的示例空间特征; 最后用 Gaussian RBF核将包空间和示例空间特征映射到相同的特征空间, 采用基于权重的特征融合方法进行特征融合. 在多示例的基准数据集、公开的图像数据集和文本数据集上进行实验的结果表明, 该方法提高了分类效果.%In terms of multi-instance learning, it is difficult for the bag-space features to capture local information of the bag. And it is also difficult for instance-space features to capture global and structural information of the bag. We proposed a multi-instance learning method to fuse bag-space features with instance-space features to solve the above problems. Firstly, we established a graph model that described structural relations among in-stances in a bag. The graph model was transformed as an affinity matrix which could be used as the bag-space features. Secondly, we selected the instances in the positive bags. The features of the instances would be regarded as the instance-space features, if the correlation between those instances and the category of the positive bag was relatively strong. And we selected the instances in the negative bags. The features of the instances would be re-garded as the instance-space features of the negative bags, if the correlation between those instances and the cat-egory of the positive bag was weaker. Finally, we used the Gaussian RBF kernel to map the bag-space features and the instance-space features to the same feature space. Then we used the feature fusion method based on the weight to fuse the two kinds of features in the same feature space. The experimental results on benchmark data set for multi-instance learning, public image data set and text data set show that the classification performance is im-proved by the proposed method.
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