摘要
Abstract (Summary)In this dissertation we discuss learning with structures, which appears frequently in both machine learning theories and applications. First we review existing structure learning algorithms, then we study several specifically interesting problems. The first problem we study is the structure learning of dynamic systems. We investigate using dynamic Bayesian networks to reconstruct functional cortical networks from the spike trains of neurons. Next we study structure learning from matrix factorization, which has been a popular research area in recent years. We propose an efficient non-negative matrix factorization algorithm which derives not only the membership assignments to the clusters but also the interaction strengths among the clusters. Following that we study the hierarchical and grouped structure in regularization. We propose a novel regularizer called group lasso which introduces competitions among variables in groups, and thus results in sparse solutions. Finally we study the sparse structure in a novel problem of online feature selection, and propose an online learning algorithm that only needs to sense a small number of attributes before the reliable decision can be made.
摘要译文
摘要（摘要）本文我们讨论学习与结构，频繁地出现在两个机器学习理论和应用。首先，我们回顾现有的结构学习算法，那么我们研究的几个特别有趣的问题。我们研究的第一个问题是动力系统的结构学习。我们调查使用动态贝叶斯网络重构功能网络皮质神经元从的穗列车。下一步，我们研究的结构学习从矩阵分解，这一直是一个热门的研究领域在最近几年。我们提出了一种有效的非负矩阵分解算法派生不仅成员分配到簇，而且该簇之间的相互作用强度。以下是我们在研究正规化的分层和分组的结构。我们提出了一个新的名为正规化套索组介绍了该组中变量之间的竞争，从而导致稀疏的解决方案。最后，我们研究了稀疏结构在一个新颖的在线特征选择的问题，并且提出了一个在线学习算法，仅需要感测少量的属性可制成可靠决定。
Zhou, Yang. Learning with structures[D]. US: Michigan State University, 2011