特征空间中的稀疏表示及其分类研究

项目名称： 特征空间中的稀疏表示及其分类研究

项目编号： No.61472138

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： 范自柱

作者单位： 华东交通大学

项目金额： 80万元

中文摘要： 近年来，基于稀疏表示理论的分类方法在模式识别与机器学习等领域得到了广泛的关注。它对遮挡等噪声具有很强的鲁棒性，在图像识别如人脸识别等高维应用领域取得了较好的效果。不过，此类方法取得好的分类效果往往需要两个假设条件。第一，稀疏表示求解须是充分稀疏的。第二，属于不同类的样本向量需分布在不同方向上。这些假设在实际应用中是很难同时满足的，而且还会遇到其他情况，如样本维数低而训练样本数很多，经典的基于稀疏表示理论的分类方法将难以取得好的分类效果。为解决上述问题，本课题拟将原始输入空间中的样本映射到高维特征空间中，采用稀疏表示理论对样本分类，并重点研究数据维数与训练样本个数之间的关系，以及此类方法中涉及的字典学习。通过此课题的研究，将会大大丰富基于稀疏表示的分类方法的理论体系，并将扩大这种分类方法的应用范围，使其既能处理高维数据又能对低维样本表示并分类。

中文关键词： 稀疏表示；分类；特征空间

英文摘要： In recent years, sparse representation based classification method has attracted wide attention in patter recogntion and machine learning community. It is very robust to the noise such as the occlusion in the image. This method can achieve good classification result in high dimensional applications such as face recognition. However, sparse representation based classification method usually needs two assumptions. The first assumption is that the resolution of the sparse representation needs to be sufficiently sparse. The second one is that sample vectors belonging to different classes should distribute on different vector directoins. These assumptions are often satisfied in the real-world applications. Moreover, we may encounter the other learning situations. The typical sparse representation based classification method may fail to achieve desirable classification performance under the condition that the sample dimensionality is very low whereas the training set is very large-scale. In order to address the above problems, this research project will map the samples of the original input space into high dimensional feature space, and exploit sparse representation theory to classify the samples. We also investigate the relationship between the data dimensionality and the nunber of the training samples, as well as the dictionary learning involved in the method. This research will greatly enrich the theory of the sparese representation based classification, and enlarge the application areas of this classification approach. It can deal well with both high dimensional samples and low dimensional samples, and then classify them.

英文关键词： sparse representation;classification;feature space