嫁接光:马尔可夫随机场的快速增量特征选择和结构学习Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields |
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课程网址: | http://videolectures.net/kdd2010_zhu_glfi/ |
主讲教师: | Jun Zhu |
开课单位: | 清华大学 |
开课时间: | 2010-10-01 |
课程语种: | 英语 |
中文简介: | 为了在高维学习中实现更好的可推广性,特征选择是一项重要任务,马尔可夫随机场(MRF)的结构学习可以自动发现复杂数据的内在结构。这两个问题都可以归结为解决ℓ1-范数正则化参数估计问题。要解决这样的问题ℓ1-正则化估计问题,现有的Grafting[16]方法可以通过增量选择新特征来避免在结构学习中对密集图进行推断。然而,一旦包含新的特征,Grafting就会执行贪婪的步骤来优化自由参数。当参数学习本身不重要时,例如在MRF中,这种贪婪策略导致效率低下,其中参数学习依赖于昂贵的子程序来计算梯度。在MRF中计算梯度的复杂性通常与最大集团的大小成指数关系。在本文中,我们提出了一种称为嫁接光的快速算法来解决ℓ用于有效特征选择和结构学习的MRF的1-范数正则化最大似然估计。嫁接光迭代地在自由参数上执行一步顺向梯度下降,并选择新的特征。这种懒惰策略保证收敛到全局最优,并可以有效地选择重要特征。在合成数据集和真实数据集上,我们表明,在特征选择和结构学习方面,嫁接光比嫁接更有效,并且与用于特征选择的最优批处理方法相比,但在MRF的结构学习方面更有效和准确。 |
课程简介: | Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automatically discover the inherent structures underlying complex data. Both problems can be cast as solving an ℓ1-norm regularized parameter estimation problem. To solve such an ℓ1-regularized estimation problem, the existing Grafting [16] method can avoid doing inference on dense graphs in structure learning by incrementally selecting new features. However, Grafting performs a greedy step of optimizing over free parameters once new features are included. This greedy strategy results in low efficiency when parameter learning is itself non-trivial, such as in MRFs, in which parameter learning depends on an expensive subroutine to calculate gradients. The complexity of calculating gradients in MRFs is typically exponential to the size of maximal cliques. In this paper, we present a fast algorithm called Grafting- Light to solve the ℓ1-norm regularized maximum likelihood estimation of MRFs for efficient feature selection and structure learning. Grafting-Light iteratively performs one-step of orthant-wise gradient descent over free parameters and selects new features. This lazy strategy is guaranteed to converge to the global optimum and can effectively select significant features. On both synthetic and real data sets, we show that Grafting-Light is much more efficient than Grafting for both feature selection and structure learning, and performs comparably with the optimal batch method for feature selection but is much more efficient and accurate for structure learning of MRFs. |
关 键 词: | 高维学习; 正则化估计; 指数关系 |
课程来源: | 视频讲座网 |
数据采集: | 2023-03-09:chenjy |
最后编审: | 2023-03-09:chenjy |
阅读次数: | 27 |