基于L21范数的稀疏鉴别子空间学习

项目名称： 基于L21范数的稀疏鉴别子空间学习

项目编号： No.61305036

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 徐洁

作者单位： 广东工业大学

项目金额： 22万元

中文摘要： 稀疏鉴别向量能实现对高维数据的关键特征的感知和理解、并为有用的鉴别特征的采集和提取提供依据和保障。具有行稀疏特点的联合稀疏鉴别向量有更强的鲁棒性和更好的可解释性，并能达到一致的特征选择功能和一致解释特性。本项目将引入联合稀疏的机制，构建基于图保持、边界最大化，以及分类性能最优化等问题的三类集特征选择与提取于一体的联合稀疏子空间学习框架。本项目的特色在于：将人类感知图像的稀疏性机制与模式识别的研究结合起来，探索联合稀疏的理论和学习方法，并从子空间学习的角度推动模式识别相关理论与算法的发展，探索出更符合人类认知方式的识别系统。相关的研究成果在基因表达、图像识别、病理分析、金融信息处理等领域都有非常重要的应用价值。

中文关键词： 稀疏；子空间学习；模糊集理论；；

英文摘要： The key features of high-dimensional data can be perceived and understood from the sparse discriminative vector, by which the discriminative features can be selected and collected in a reliable way. Compared with the sparse discriminative vectors, the joint sparse discriminative vectors is row-sparse, and is more robust and explanative. It is going to introduce the joint sparse in the following three aspects: Graph preserving, margin maximizing and performance of classification optimizing. The characteristics of this research project are that the sparsity mechanism of human perception of images and the feature extraction theorems are combined together to explore the theory of joint sparse. In such a way, the related theories of pattern recognition can be developed. In addition, the proposed theorems and methods could be widely used in the field of image recognition, genetic data analysis, pathological analysis, financial information processing, and so on.

英文关键词： sparse；subspace learning；fuzzy set theory；；