基于知识迁移的有限样本模式分类研究

项目名称： 基于知识迁移的有限样本模式分类研究

项目编号： No.61472424

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 其他

项目作者： 王雪松

作者单位： 中国矿业大学

项目金额： 82万元

中文摘要： 在许多实际应用场合，对所有类别的样本进行人工标注是一件费时、费力，甚至是不可能的事情。为此，拟采用知识迁移方法，针对有限训练样本（单样本、零样本）学习场景下的模式分类问题展开研究。内容包括：将人脸数据库中具有与目标训练或测试样本相同或相近宏观特征的样本视为迁移源，研究基于特征迁移的单样本人脸识别；为在零样本学习场景下充分利用与属性相关的先验知识，首先，采用结构化学习方法挖掘属性与属性间存在的内在联系，构建多属性联合预测模型。其次，借鉴多任务学习中特征选择的思路，将属性与底层特征间的联系融入进分类器的设计之中；为克服相对属性需要假定所有未见类图像和可见类图像均服从特定分布的局限性，将相对属性与决策树相结合，进而构造随机森林，实现零样本学习场景下的图像分类。研究成果不仅可以丰富现有的机器学习理论和方法，而且能够广泛推广应用到模式分类的诸多相关领域，具有重大理论意义和实用价值。

中文关键词： 模式分类；单样本；零样本；知识迁移；属性

英文摘要： In many practical application occasions, labeling samples for all classes is time-consuming, laborious and even impossible. Therefore, the pattern classification problem under the learning scene of limited training samples (single-sample or zero-sample) will be researched using knowledge transfer methods in the project. The main contents in our research include the following aspects. Single-sample face recognition based on feature transfer is researched by viewing the samples contained in the face database as transfer sources, which have the same or similar macro facial appearance features as or with the single target training or testing sample. In order to take full advantage of prior knowledge related with attributes under the zero-sample classification scene, two kinds of methods are proposed. Firstly, the intrinsic relation between attributes is mined using structured learning methods and then a multi-attribute joint prediction model is built. Secondly, the intrinsic relation between attributes and low-level features is integrated into the classifier design by borrowing the idea of feature selection existed in multi-task learning. When relative attributes are applied to zero-sample classification problems, all seen and unseen images should satisfy specific distributions. In order to overcome the limitation of relative attributes, the relative attribute is combined with decision tree and then a random forest is built to realize the image classification under zero-sample learning scene. The research fruits not only can enrich the present theory and methods of machine learning, but also can be widely extended to many related fields of pattern classification. Therefore, the research has important theoretical significance and practical value.

英文关键词： Pattern classification;Single-sample;Zero-sample;Knowledge transfer;Attribute