基于稀疏表示和流形理论的半监督分类研究

项目名称： 基于稀疏表示和流形理论的半监督分类研究

项目编号： No.11426159

项目类型： 专项基金项目

立项/批准年度： 2015

项目学科： 数理科学和化学

项目作者： 古楠楠

作者单位： 首都经济贸易大学

项目金额： 3万元

中文摘要： 科学技术的发展使人们能够方便快捷地获取各种数据，但要获取大量有类别标签的数据则比较困难。半监督分类能充分利用少量有标签数据与大量无标签数据进行分类，是近年来数据分析领域中的热点方法之一。本项目拟针对半监督分类中的两个重要问题进行研究，(1)针对半监督分类的模型假设问题，拟提出稀疏化模型假设并研究其合理性，同时突破现有稀疏分类方法面对非图像数据时的局限性，提出针对一般自然数据的，具有显性表达式的稀疏半监督分类方法；(2)针对半监督分类在低质量图像人脸识别中的应用问题，拟将图像数据局部流形特征与全局稀疏特征相结合，提出判别性的字典学习方法来获得数据稀疏表示，进而构造对图像噪声及遮挡问题有较好稳健性的半监督分类方法。所获结果将不仅在理论层面促进半监督分类的发展，也有望在应用层面扩展半监督分类方法对低质量图像数据的适用能力，因而具有重要的理论意义与应用前景。

中文关键词： 半监督分类；稀疏表示；特征选择；视线估计；

英文摘要： With the development of science and technology, people can conveniently get various kinds of data, but it is usually difficult to obtain large quantities of labeled data. Semi-supervised classification can utilize labeled and unlabeled data simultaneously to train the classifier, and has become one of the hottest methods in data analysis field. In this project, we plan to focus on two important issues in semi-supervised classification. (1) For the model assumption problem, we plan to propose the sparsity based model assumption and study its rationality. Then, construct the sparsity based semi-supervised classification method that has explicit expression for general natural data, to break through the limitation of existing sparsity based classification methods when facing non-image data. (2) For the application of semi-supervised classification on low-quality images based face recognition, we desire to combine the local manifold feature of data with the global sparsity feature, and make use of discriminant dictionary learning to get the sparse representation of low-quality images; then build the classifier that is robust with image noise and occlusion. The desired achievements will not only promote the development of semi-supervised classification in theory, but also in practice extend the ability that applying s

英文关键词： semi-supervised classification；sparse representation；feature selection；gaze estimation；