结构化判别字典学习方法及其应用研究

项目名称： 结构化判别字典学习方法及其应用研究

项目编号： No.61272331

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 向世明

作者单位： 中国科学院自动化研究所

项目金额： 82万元

中文摘要： 判别字典学习是机器学习和模式识别中的一个前沿性课题。现有算法在分类性能、模型训练等方面还不能满足现实的应用需求。本项目拟研究结构化判别字典学习建模方法和算法理论，形成包含结构化稀疏判别字典学习、自导判别字典学习和多视角判别字典学习等在内的字典学习算法。在学习模型构建中，基于监督信息和聚类信息，项目将引入结构化约束来提高字典学习模型的分类判别能力；同时，研究形式紧凑的最大间隔多类分类判别回归模型的构建方法，并将其作为分类器嵌入到判别字典学习模型之中，保证分类性能的同时降低模型复杂度。此外，项目还将构建能够统一多种范数的函数表示模型，以此为基础实现判别字典学习模型的高效求解。 　　另外，对本项目提出的算法，拟应用于基于样例的图像感兴趣目标分割和图像超像素分割。为此，项目拟开发一个图像分割实验平台，在此平台上验证和分析项目提出的学习算法，提高图像分割的精度和速度，进而促进学习算法的应用研究。

中文关键词： 判别最小二乘回归；稀疏学习；字典学习；深度学习；图像分割

英文摘要： Discriminative Dictionary Learning (DDL) is one of the frontier topics in the fields of machine learning and pattern recognition. Up to now, researcheres have developed a few DDL approaches under the sparse representation framework. However, most of them fail to generate high accurate classification in many realworld situations. Meanwhile, training their learning models may cost a large amount of computation time. To address these issues, this project targets at developing a new class of DDL models in a distinguishing way of structural modeling. In this process, theoretical analyses behind the modeling will be conducted to construct and optimize the DDL models. Specifically, the research work will mainly focus on developing the following learning models, inclduing structured sparse DDL model, self-taught DDL model, and multi-view DDL model. Studying with these models, a new class of DDL algorithms will be finally proposed. To this end, we will study how to derive and formulate structured constraints from supervised information and clusters, so as to improve the discriminative ability of the DDL models. In addition, to embed an effective and efficient classifier into the DDL models to be constructed, we will study how to develop a discriminative regression model with compact form under the large margin framework

英文关键词： Discriminative least squares regression；Sparse learning；Dictionary learning；Deep learning；Image segmentation