属性学习及其应用研究

项目名称： 属性学习及其应用研究

项目编号： No.61473149

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： 张道强

作者单位： 南京航空航天大学

项目金额： 83万元

中文摘要： 属性是对目标对象的一种语义刻画，因其灵活性和可解释性等优点，属性学习已成为近年来机器学习和模式识别等领域的一个新的研究热点。本项目旨在原有工作基础之上，对属性学习中存在的属性表示、属性关系学习和属性分类模型设计等重要问题进行研究。具体地，本项目将：（1）构建基于最大类分离度和最小属性冗余准则的属性表示模型,并进一步设计具有低秩和稀疏结构的属性选择方法，从而避免人工定义属性存在的成本高且缺乏判别性等缺陷；（2）提出自动属性关系学习和排他性共享特征选择方法，利用逆协方差矩阵直接从数据中挖掘属性间关系并用于低层特征选择；（3）设计面向特定目标类的个性化属性分类及双重代价敏感属性分类方法，实现目标类的精确属性描述并克服属性分类中的类别不平衡问题；（4）把上述属性学习模型和方法应用于脑影像分析及脑疾病早期诊断。通过本项目的研究不仅能在属性学习理论与方法上有所贡献，还可望取得实际的应用成果。

中文关键词： 属性学习；脑影像分析；特征选择；稀疏学习；代价敏感分类

英文摘要： Attribute is a kind of semantic characterization of target objects. Because attributes have the advantages of flexibility and interpretability in describing objects, attribute-based learning has become a new hot topic in the machine learning and pattern recognition communities recently. Based on our previous works, in this project we will study several important problems in attribute-based learning, including attribute representation, attribute relationship learning and design of atrribute classificaiton models. Specifically, in this project we will: 1) construct a novel attribute representation model based on the maximum class seperation and minimum attribute redundance criterion, and further develop an attribute selection method with a low-rank and sparse structure, thus avoiding the defects of high cost of labor and low discriminative power in traditional human-defined attributes; 2) propose an automatic attribute relationship learning method and an exclusively sharing-based feature selection method, where we learn an inverse covariance matrix directly from data to mine the relathionships among attributes and then use it for low-level feature selection; 3) design a personlized attribute classification model for specific target class and a two-stage cost-sensitive attribute classification method, to achieve accurate atrribute description of the target class and to overcome the class-imbalance problem in attribute classification, respectively; 4) apply the above-mentioned attribute-based learning models and methods for brain imaging analysis and early diagnosis of brain diseases. The study of this project will contribute to the theory and method of attribute-based learning, and is also expected to achive practical application results.

英文关键词： Attribute-based Learning;Brain Imaging Analysis;Feature Selection;Sparse Learning;Cost-Sensitive Classification