文档介绍：Online Feature Selection for Mining Big Data 挖掘大数据的在线特征选择 Steven . Hoi ?, Jialei Wang ?, Peilin Zhao ?, Rong Jin ?? School puter Engineering, Nanyang Technological University, Singapore ? Department puter Science and Engineering, Michigan State University, USA {chhoi, , zhao0106}***@, ******@ ABSTRACT 摘要 Most studies of online learning require accessing all the attributes/ features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features are active and can be used for prediction. We address this challenge by studying sparsity regularization and truncation techniques. Specifically, we present an effective algorithm to solve the problem, give the theoretical analysis, and evaluate the empirical performance of the proposed algorithms for online feature selection on several public datasets. We also demonstrate the application of our online feature selection technique to tackle real-world problems of big data mining, which is significantly more scalable than some well-known batch feature selection algorithms. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques for large-scale applications. 大多数在线学****的研究需要访问所有的属性/培训实例特点。这样一个经典的设置并不总是适用于真实世界的应用当数据实例的高维或访问它是昂贵的,以获得全套的属性/功能。为了解决这个限制,我们调查的问题在线特征选择( OFS ),在线学只允许保持一个分类涉及一个小的和固定数目。在线功能的关键挑战选择是如何使用一个准确的预测小型和固定数量的活动特征。这是对比以经典的在线学****的设置, 所有的功能都是主动的,可用于预测。我们解决这个问题研究稀疏正则化和截断的挑战技术。具体而言, 我们提出了一种有效的算法解决问题,给出理论分析和评价建议算法的经验性能几种公共数据集的在线特征选择。我们