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大型稀疏Logistic回归

Large-Scale Sparse Logistic Regression
课程网址: http://videolectures.net/kdd09_liu_lsslr/  
主讲教师: Jieping Ye
开课单位: 密歇根大学
开课时间: 2009-09-14
课程语种: 英语
中文简介:
Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the L1-norm regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving large-scale sparse logistic regression. Specifically, we formulate the problem as the L1-ball constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems.
课程简介: Logistic回归方法，已被广泛应用于数据挖掘，机器学习，计算机视觉的许多应用中一个著名的分类方法，生物信息学。稀疏Logistic回归嵌入特征选择使用L1范数正则化分类框架，是有吸引力的许多应用涉及高维数据。在本文中，我们提出了求解大规模稀疏Logistic回归lassplore。具体来说，我们制定的问题作为L1球约束光滑凸优化，并提出使用涅斯捷罗夫的方法解决这个问题，一个最佳的一阶光滑的凸优化的黑盒方法。在涅斯捷罗夫的方法使用的关键问题之一是在每一个优化迭代步长的估计。以前的方法适用于恒定步长假设Lipschitz梯度是预先已知的，或需要一系列递减的步长，导致收敛速度慢，在实践中。在本文中，我们提出了一种自适应的线搜索方案，允许调整的步长自适应和最优收敛速度的同时保证。几个国家的最先进的算法的实证比较表明所提出的算法的效率，对大规模问题lassplore。
关 键 词: Logistic回归方法; 涅斯捷罗夫; 黑盒
课程来源: 视频讲座网
最后编审: 2020-06-07：王勇彬(课程编辑志愿者)
阅读次数: 734

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