数据内在结构和稀疏保持的大间隔分类方法研究

项目名称： 数据内在结构和稀疏保持的大间隔分类方法研究

项目编号： No.61502208

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 苟建平

作者单位： 江苏大学

项目金额： 20万元

中文摘要： 基于稀疏表示的模式分类是机器学习领域的研究热点。随着稀疏表示应用范围的拓展，面对大量新出现的具有复杂结构特征的高维数据分析问题，传统的稀疏表示分类器与稀疏子空间学习在其准确性、鲁棒性和泛化能力问题上均面临着新的挑战。本项目拟从数据内在结构着手，基于大间隔学习思想，研究数据内在结构和稀疏保持的大间隔分类新方法，旨在提高算法在复杂数据结构下的准确性、鲁棒性、泛化能力，进而增强数据表示能力和判别能力。主要研究内容包括：数据内在结构保持的大间隔稀疏表示分类器；数据内在结构保持的大间隔稀疏子空间学习；基于大间隔的数据内在结构和稀疏保持的降维过程与分类器的同步学习。通过本项目的研究，预期获得适应数据结构特征的稀疏表示分类与降维新理论与方法，为推动稀疏表示的发展和应用，以及大数据分析提供新的理论支撑。

中文关键词： 机器学习；模式分类；子空间学习；稀疏表示

英文摘要： Sparse representation based classification has been the research hotspots in the field of machine learning. With the extension of applications of sparse representation, the traditional classification and subspace learning methods based on sparse representation face the new challenges on the accuracy, robustness and generalization problems when there are many high-dimensional data analysis issues that newly appear with the complex structure features of data. Starting from the data intrinsic structure and based on large margin idea, this project is to investigate the new max-margin classification approaches that preserve data intrinsic structure and sparsity, in order to improve the accuracy, robustness, generalization and the abilities of data representation and discrimination in the case of the complex data structure. The research content of this project includes: a study of the data intrinsic structure and sparsity preserving max-margin classification, a study of the data intrinsic structure and sparsity preserving max-margin subspace learning, a study of synchronous learning that jointly integrates dimensionality reduction and classifiers by preserving data intrinsic structure and sparsity on basis of large margin. Through the research of this project, the new sparse representation based dimensionality reduction and classification methods that are adaptive to data structure characteristics will be achieved. The research of this project offers the new theoretical support for the development and applications of sparse representation and big data analysis.

英文关键词： Machine Learning;Pattern Classification;Subspace Learning;Sparse Representation