CASIA OpenIR  > 毕业生  > 博士学位论文
Thesis Advisor刘成林 研究员 ; 勇研究员
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword信息论 特征选择 稀疏 合页损失 非凸非光滑优化技术
1) 从信息论的基本概念出发,发现了 Fano 不等式之外另一种全新的 Bayes 错误率和关于特征与类别的互信息量的关系。为现有的由不同启发式准则发展出的基于信息论的算法建立了一个统一的理论框架。在此框架内,受 Occam 剃刀原理启发提出一种新的基于信息论的特征选择算法。该算法还可以通过嵌入一个插件用于辨别冗余和噪声特征以更好地进行特征选择。该算法的有效性在实验中得到了充分的验证。
2) 受稀疏表示和支持向量机的启发,提出了一种基于不等式约束的 l_{2,p} 范数 (0
3) 提出一种建立在 l_{2,r} 范数 (0
4) 提出了一种建立在新的合页损失和 l_{2,p} 范数 (0
Other Abstract
With computerization increasingly penetrating into various social sectors,  data in these sectors is accumulated with accelerated rate. How to exploit the underlying valuable information from  "big data" is a great challenge. Large-scale data is commonly highly dimensional. Actually, there are various redundant and noisy features in the data. The redundancy and noise not only waste  storage resources, but also lower the running efficiency of leaning models.  More seriously, The redundancy and noise may inundate the useful information and deteriorate the performance of learning models. Feature selection approaches is proposed to address these problems, which is the process of selecting a compact subset form original full features of data with total or most of intrinsic information conserved.
In this dissertation, from the perspective of information theory and sparsity,  we carry out a series of specific and insightful researches for feature selection, which are focused on the target that removing redundant and noisy features and select a compact feature subset. The main novelties and contributions are list as follow:
1) Based on information theory, we discovered a new relationship of Bayesian error and the mutual information between features and class labels, except of Fano's Inequality. We constructed a unified framework for existing popular information theoretical methods based on heuristic. Inspired by the principle of Occam's Razor, we proposed a new information theory based feature selection approach. Another advantage of the proposed method is that it could integrate a plug-in component to distinguish redundant features and noisy features. The encouraged experimental results indicates the validity of the proposed method.
2) Motivated by sparsity representation and support vector machine(SVM), we proposed a feature selection medel that is built on l_{2,p}-norm (0
3) We proposed a general sparsity regularized feature selection model that is built on a l_{2,r}-norm (0
4) We proposed a feature selection model that is built upon a new extended hinge loss and a l_{2,p}-norm (0
Document Type学位论文
Recommended Citation
GB/T 7714
彭涵阳. 基于信息论与稀疏性的特征选择算法研究[D]. 北京. 中国科学院研究生院,2017.
Files in This Item:
File Name/Size DocType Version Access License
dissertation.pdf(3743KB)学位论文 限制开放CC BY-NC-SA
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[彭涵阳]'s Articles
Baidu academic
Similar articles in Baidu academic
[彭涵阳]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[彭涵阳]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.